13.4.1.9 Computation and Analysis of Principal Components, Eigen Values, SVD

Shlien, S.,
A Method for Computing the Partial Singular Value Decomposition,
PAMI(4), No. 6, November 1982, pp. 671-676. BibRef 8211

Haimi-Cohen, R., and Cohen, A.,
Gradient-Type Algorithms for Partial Singular Value Decomposition,
PAMI(9), No. 1, January 1987, pp. 137-142. BibRef 8701

Nayar, S.K.[Shree K.], Murase, H.[Hiroshi],
Dimensionality of Illumination Manifolds in Appearance Matching,
ORCV96(165) 9611
BibRef

Roseborough, J.B.[James B.], Murase, H.[Hiroshi],
Partial eigenvalue decomposition for large image sets using run-length encoding,
PR(28), No. 3, March 1995, pp. 421-430.
Elsevier DOI 0401
BibRef

Murase, W., Lindenbaum, M.,
Partial eigenvalue decomposition of large images using spatial temporal adaptive method,
IP(4), No. 5, May 1995, pp. 620-629.
IEEE DOI 0402
BibRef

Oja, E.,
A simplified neuron model as a principal component analyzer,
J. Math. Biol.(15), 1982, pp. 267-273. Neural Networks. BibRef 8200

Oja, E.,
Neural networks, principal components, and subspace,
IJNS(1), 1989, pp. 61-68. BibRef 8900

Oja, E.,
Principal components, minor components, and linear neural networks,
NeurNet(5), 1992, pp. 927-935. BibRef 9200

Lorenzelli, F., Yao, K.,
Updating the Jacobi SVD for Nonstationary Data,
VISP(144), No. 2, April 1997, pp. 108-115. 9706
BibRef

Chandrasekaran, S., Manjunath, B.S., Wang, Y.F., Winkeler, J., Zhang, H.,
An Eigenspace Update Algorithm for Image Analysis,
GMIP(59), No. 5, September 1997, pp. 321-332.
PDF File. 9712
BibRef
Earlier: TRUniv. of California Santa Barbara. 1995. BibRef
And: A2, A1, A3 Only: SCV95(551-556).
IEEE DOI
PDF File. Introduce the vision community to the eigenspace updating algoithms from signal processing.
HTML Version. BibRef

Boyle, R.D.[Roger D.],
Scaling additional contributions to principal components analysis,
PR(31), No. 12, December 1998, pp. 2047-2053.
Elsevier DOI BibRef 9812

Zhao, L.[Li], Yang, Y.H.[Yee-Hong],
An efficient algorithm to compute eigenimages in PCA-based vision systems,
PR(32), No. 5, May 1999, pp. 851-864.
Elsevier DOI BibRef 9905

Yang, T.N.[Tai-Ning], Wang, S.D.[Sheng-De],
Robust algorithms for principal component analysis,
PRL(20), No. 8, August 1999, pp. 927-933. BibRef 9908

Horgan, G.W.[Graham W.],
Principal Component Analysis of Random Particles,
JMIV(12), No. 2, April 2000, pp. 169-175.
DOI Link 0002
BibRef

Havlicek, J.P., Harding, D.S., Bovik, A.C.,
Multidimensional Quasi-Eigenfunction Approximations and Multicomponent AM-FM Models,
IP(9), No. 2, February 2000, pp. 227-242.
IEEE DOI 0003
BibRef

Hall, P.M.[Peter M.], Marshall, D.R.[David R.], Martin, R.R.[Ralph R.],
Merging and Splitting Eigenspace Models,
PAMI(22), No. 9, September 2000, pp. 1042-1049.
IEEE DOI 0010
Method to merge and split models. BibRef

Hall, P.M., Marshall, D.R., Martin, R.R.,
Incremental Eigenanalysis for Classification,
BMVC98(xx-yy). BibRef 9800

Hall, P.M.[Peter M.], Marshall, D.R.[David R.], Martin, R.R.[Ralph R.],
Adding and subtracting eigenspaces with eigenvalue decomposition and singular value decomposition,
IVC(20), No. 13-14, December 2002, pp. 1009-1016.
Elsevier DOI 0212
BibRef
Earlier:
Adding and subtracting eigenspaces,
BMVC99(Models and Search).
PDF File. BibRef

Bach, F.R., and Jordan, M.I.,
Kernel independent component analysis,
MachLearnRes(3), 2002, pp. 1-48.
DOI Link BibRef 0200

p Chang, C.Y., Maciejewski, A.A., Balakrishnan, V.,
Fast Eigenspace Decomposition of Correlated Images,
IP(9), No. 11, November 2000, pp. 1937-1949.
IEEE DOI 0011
BibRef

Saitwal, K., Maciejewski, A.A., Roberts, R.G., Draper, B.A.,
Using the Low-Resolution Properties of Correlated Images to Improve the Computational Efficiency of Eigenspace Decomposition,
IP(15), No. 8, August 2006, pp. 2376-2387.
IEEE DOI 0606
BibRef

Saitwal, K., Maciejewski, A.A., Roberts, R.G.,
Eigendecomposition of Correlated Images Characterized by Three Parameters,
Southwest06(203-207).
IEEE DOI 0603
BibRef

Donoho, D.L., Huo, X.M.[Xiao-Ming],
Uncertainty Principle and Ideal Atomic Decompositions,
IT(47), No. 7, November 2001, pp. 2845-2862. Decompose image with pairs of bases. BibRef 0111

Yang, J.[Jian], Yang, J.Y.[Jing-Yu],
From image vector to matrix: A straightforward image projection technique: IMPCA vs. PCA,
PR(35), No. 9, September 2002, pp. 1997-1999.
Elsevier DOI 0206

See also Face recognition based on the uncorrelated discriminant transformation. BibRef

Yang, J.[Jian], Zhang, D.[David], Frangi, A.F.[Alejandro F.], Yang, J.Y.[Jing-Yu],
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition,
PAMI(26), No. 1, January 2004, pp. 131-137.
IEEE Abstract. 0401
PCA applied to a 2-D matrix rather than conversion to 1-D matrix. BibRef

Xu, Y., Zhang, D.[David], Yang, J.[Jian], Yang, J.Y.[Jing-Yu],
A Two-Phase Test Sample Sparse Representation Method for Use With Face Recognition,
CirSysVideo(21), No. 9, September 2011, pp. 1255-1262.
IEEE DOI 1109
BibRef

Yang, M.[Meng], Zhang, L.[Lei], Yang, J., Zhang, D.[David],
Regularized Robust Coding for Face Recognition,
IP(22), No. 5, May 2013, pp. 1753-1766.
IEEE DOI 1303

See also Gabor Feature Based Robust Representation and Classification for Face Recognition with Gabor Occlusion Dictionary. BibRef

Yang, J.[Jian], Jin, Z.[Zhong], Yang, J.Y.[Jing-Yu], Zhang, D.[David], Frangi, A.F.[Alejandro F.],
Essence of kernel Fisher discriminant: KPCA plus LDA,
PR(37), No. 10, October 2004, pp. 2097-2100.
Elsevier DOI 0409
BibRef

Yang, J.[Jian], Frangi, A.F.[Alejandro F.], Yang, J.Y.[Jing-Yu], Zhang, D.[David], Jin, Z.[Zhong],
KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition,
PAMI(27), No. 2, February 2005, pp. 230-244.
IEEE Abstract. 0501
BibRef

Yang, J.[Jian], Zhang, D.[David], Jin, Z.[Zhong], Yang, J.Y.[Jing-Yu],
Unsupervised Discriminant Projection Analysis for Feature Extraction,
ICPR06(I: 904-907).
IEEE DOI 0609
BibRef

Zuo, W.M.[Wang-Meng], Zhang, D.[David], Wang, K.Q.[Kuan-Quan],
An assembled matrix distance metric for 2DPCA-based image recognition,
PRL(27), No. 3, February 2006, pp. 210-216.
Elsevier DOI 0512
BibRef

Zuo, W.M.[Wang-Meng], Zhang, D.[David], Wang, K.Q.[Kuan-Quan],
Bidirectional PCA With Assembled Matrix Distance Metric for Image Recognition,
SMC-B(36), No. 4, August 2006, pp. 863-872.
IEEE DOI 0606
BibRef
Earlier: A1, A3, A2:
Bi-Directional PCA with Assembled Matrix Distance Metric,
ICIP05(II: 958-961).
IEEE DOI 0512
BibRef

Wang, F.Q.[Fa-Qiang], Zhang, H.Z.[Hong-Zhi], Wang, K.Q.[Kuan-Quan], Zuo, W.M.[Wang-Meng],
Fast neighbourhood component analysis with spatially smooth regulariser for robust noisy face recognition,
IET-Bio(3), No. 4, 2014, pp. 278-290.
DOI Link 1504
face recognition BibRef

Elad, M., Bruckstein, A.M.,
A Generalized Uncertainty Principle and Sparse Representation in Pairs of Bases,
IT(48), No. 9, September 2002, pp. 2558-2567. BibRef 0209

Altamirano, L.C.[Luis Carlos], Altamirano, L.[Leopoldo], Alvarado, M.[Matías],
Non-uniform sampling for improved appearance-based models,
PRL(24), No. 1-3, January 2003, pp. 521-535.
Elsevier DOI 0211
BibRef

Hamdan, R.[Raouf], Heitz, F.[Fabrice], Thoraval, L.[Laurent],
A low complexity approximation of probabilistic appearance models,
PR(36), No. 5, May 2003, pp. 1107-1118.
Elsevier DOI 0301
BibRef

Wang, Z.[Ze], Lee, Y.[Yin], Fiori, S.[Simone], Leung, C.S.[Chi-Sing], Zhu, Y.S.[Yi-Sheng],
An improved sequential method for principal component analysis,
PRL(24), No. 9-10, June 2003, pp. 1409-1415.
Elsevier DOI 0304
BibRef

Roberts, S.[Stephen], Choudrey, R.[Rizwan],
Data decomposition using independent component analysis with prior constraints,
PR(36), No. 8, August 2003, pp. 1813-1825.
Elsevier DOI 0304
BibRef

Melzer, T.[Thomas], Reiter, M.[Michael], Bischof, H.[Horst],
Appearance Models Based on Kernel Canonical Correlation Analysis,
PR(36), No. 9, September 2003, pp. 1961-1971.
Elsevier DOI 0307
BibRef

Jogan, M., Zagar, E., Leonardis, A.,
Karhunen-Loeve expansion of a set of rotated templates,
IP(12), No. 7, July 2003, pp. 817-825.
IEEE DOI 0308
Eigen vectors of a set of rotated templates. BibRef

Artac, M., Jogan, M., Leonardis, A.,
Incremental PCA for on-line visual learning and recognition,
ICPR02(III: 781-784).
IEEE DOI 0211
BibRef

Weng, J.Y.[Ju-Yang], Zhang, Y.L.[Yi-Lu], Hwang, W.S.[Wey-Shiuan],
Candid covariance-free incremental principal component analysis,
PAMI(25), No. 8, August 2003, pp. 1034-1040.
IEEE Abstract. 0308
Fast incremental computation method. BibRef

Davies, M., Mitianoudis, N.,
Simple mixture model for sparse overcomplete ICA,
VISP(151), No. 1, February 2004, pp. 35-43.
IEEE Abstract. 0403
Use a mixture of Gaussians. BibRef

Davies, M.,
Identifiability Issues in Noisy ICA,
SPLetters(11), No. 5, may 2004, pp. 470-473.
IEEE Abstract. 0404
BibRef

Liu, W.X.[Wei-Xiang], Zheng, N.N.[Nan-Ning],
Non-negative matrix factorization based methods for object recognition,
PRL(25), No. 8, June 2004, pp. 893-897.
Elsevier DOI 0405
BibRef
And: Erratum: PRL(26), No. 14, 15 October 2005, pp. 2313.
Elsevier DOI Non-negative matrix factorization creates nonorthonormal bases so nearest neighbor classification does not work. Adopt a Riemannian metric. BibRef

Yuan, Z.J.[Ze-Jian], Qu, Y.Y.[Yan-Yun], Yang, Y.[Yang], Zheng, N.N.[Nan-Ning],
An Approach for Constructing Sparse Kernel Classifier,
ICPR06(II: 560-563).
IEEE DOI 0609
BibRef

Eriksson, J., Koivunen, V.,
Identifiability, Separability, and Uniqueness of Linear ICA Models,
SPLetters(11), No. 7, July 2004, pp. 601-604.
IEEE Abstract. 0407
BibRef

Musa, M.E.M.[Mohamed E. M.], de Ridder, D.[Dick], Duin, R.P.W.[Robert P. W.], Atalay, V.[Volkan],
Almost autonomous training of mixtures of principal component analyzers,
PRL(25), No. 9, 2 July 2004, pp. 1085-1095.
Elsevier DOI 0407
BibRef

Lee, S., Hayes, M.H.,
Properties of the Singular Value Decomposition for Efficient Data Clustering,
SPLetters(11), No. 11, November 2004, pp. 862-866.
IEEE Abstract. 0411
BibRef

Lipovetsky, S.[Stan], Conklin, W.M.[W. Michael],
Singular value decomposition in additive, multiplicative, and logistic forms,
PR(38), No. 7, July 2005, pp. 1099-1110.
Elsevier DOI 0505
BibRef

Lipovetsky, S.[Stan],
PCA and SVD with nonnegative loadings,
PR(42), No. 1, January 2009, pp. 68-76.
Elsevier DOI 0809
Principal component analysis; Singular value decomposition; Exponential; Logit; Multinomial parameterization; Positive and sparse loadings; Perron-Frobenius theory BibRef

Chen, S.C.[Song-Can], Zhu, Y.L.[Yu-Lian], Zhang, D.Q.[Dao-Qiang], Yang, J.Y.[Jing-Yu],
Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA,
PRL(26), No. 8, June 2005, pp. 1157-1167.
Elsevier DOI 0506
BibRef

Kim, K.I.[Kwang In], Franz, M.O., Scholkopf, B.,
Iterative Kernel Principal Component Analysis for Image Modeling,
PAMI(27), No. 9, September 2005, pp. 1351-1366.
IEEE DOI 0508
Iterative estimation of PCA. Applied to super resolution and denoising. BibRef

Liang, Z.Z.[Zhi-Zheng], Shi, P.F.[Peng-Fei],
An analytical algorithm for generalized low-rank approximations of matrices,
PR(38), No. 11, November 2005, pp. 2213-2216.
Elsevier DOI 0509

See also Comments on An analytical algorithm for generalized low-rank approximations of matrices. BibRef

Nishino, K.[Ko], Nayar, S.K., Jebara, T.,
Clustered Blockwise PCA for Representing Visual Data,
PAMI(27), No. 10, October 2005, pp. 1675-1679.
IEEE DOI 0509
PCA applied to video, use spatio-temporal correlation. PCA to blocks of the data, not the whole thing. BibRef

Wang, L., Wang, X., Feng, J.,
On Image Matrix Based Feature Extraction Algorithms,
SMC-B(36), No. 1, February 2006, pp. 194-197.
IEEE DOI 0602
2DPCA and 2DLDA are equivalent to a block based feature extraction. Partition into blocks and perform PCA/LDA on aggerate of all blocks. BibRef

Gao, Q., Zhang, L., Zhang, D., Yang, J.,
Comments on 'On Image Matrix Based Feature Extraction Algorithms',
SMC-B(37), No. 5, October 2007, pp. 1373-1374.
IEEE DOI 0711

See also On Image Matrix Based Feature Extraction Algorithms. BibRef

Liu, J.[Jun], Chen, S.C.[Song-Can],
Non-iterative generalized low rank approximation of matrices,
PRL(27), No. 9, July 2006, pp. 1002-1008.
Elsevier DOI 2DPCA; Generalized low rank approximation of matrices (GLRAM); Non-iterative GLRAM (NIGLRAM); Feature extraction 0605
BibRef

Burvall, A.[Anna], Barrett, H.H.[Harrison H.], Dainty, C.[Christopher], Myers, K.J.[Kyle J.],
Singular-value decomposition for through-focus imaging systems,
JOSA-A(23), No. 10, October 2006, pp. 2440-2448.
WWW Link. 0610
BibRef

Park, S., Witten, J.M., Myers, K.J.,
Singular Vectors of a Linear Imaging System as Efficient Channels for the Bayesian Ideal Observer,
MedImg(28), No. 5, May 2009, pp. 657-668.
IEEE DOI 0905
BibRef

Witten, J.M., Park, S., Myers, K.J.,
Partial Least Squares: A Method to Estimate Efficient Channels for the Ideal Observers,
MedImg(29), No. 4, April 2010, pp. 1050-1058.
IEEE DOI 1003
BibRef

Ranade, A.[Abhiram], Mahabalarao, S.S.[Srikanth S.], Kale, S.[Satyen],
A variation on SVD based image compression,
IVC(25), No. 6, 1 June 2007, pp. 771-777.
Elsevier DOI 0704
Image compression; Singular value decomposition; Karhunen-Loeve transform; Matrix rank, Low resolution sample BibRef

Chin, T.J.[Tat-Jun], Suter, D.[David],
Incremental Kernel Principal Component Analysis,
IP(16), No. 6, June 2007, pp. 1662-1674.
IEEE DOI 0706
BibRef
Earlier:
Improving the Speed of Kernel PCA on Large Scale Datasets,
AVSBS06(41-41).
IEEE DOI 0611
BibRef
And:
Incremental Kernel PCA for Efficient Non-linear Feature Extraction,
BMVC06(III:939).
PDF File. 0609
BibRef
And:
A New Distance Criterion for Face Recognition Using Image Sets,
ACCV06(I:549-558).
Springer DOI 0601
BibRef

Chin, T.J.[Tat-Jun], Schindler, K., Suter, D.[David],
Incremental Kernel SVD for Face Recognition with Image Sets,
FGR06(461-466).
IEEE DOI 0604
BibRef

Chin, T.J.[Tat-Jun], Suter, D.[David], Wang, H.Z.[Han-Zi],
Boosting histograms of descriptor distances for scalable multiclass specific scene recognition,
IVC(29), No. 4, March 2011, pp. 241-250.
Elsevier DOI 1102
BibRef
Earlier:
Multi-structure model selection via kernel optimisation,
CVPR10(3586-3593).
IEEE DOI 1006
Keypoints; Descriptors; Distance histograms; Specific scene recognition BibRef

Chin, T.J.[Tat-Jun], Yu, J.[Jin], Suter, D.[David],
Accelerated Hypothesis Generation for Multistructure Data via Preference Analysis,
PAMI(34), No. 4, April 2012, pp. 625-638.
IEEE DOI 1203
BibRef
Earlier:
Accelerated Hypothesis Generation for Multi-structure Robust Fitting,
ECCV10(V: 533-546).
Springer DOI 1009
In the rantom hypothesis generation and test model for matching. Sampling within coherent structures. BibRef

Wong, H.S.[Hoi Sim], Chin, T.J.[Tat-Jun], Yu, J.[Jin], Suter, D.[David],
Efficient Multi-structure Robust Fitting with Incremental Top-k Lists Comparison,
ACCV10(IV: 553-564).
Springer DOI 1011
BibRef

Tanaka, A.[Akira], Imai, H.[Hideyuki], Kudo, M.[Mineichi], Miyakoshi, M.[Masaaki],
Integrated kernels and their properties,
PR(40), No. 11, November 2007, pp. 2930-2938.
Elsevier DOI 0707
Kernel; Reproducing kernel Hilbert space (RKHS); Projection learning; Parameter integration BibRef

Chang, C.C.[Chin-Chun], Lin, T.Y.[Tzung-Ying],
Linear feature extraction by integrating pairwise and global discriminatory information via sequential forward floating selection and kernel QR factorization with column pivoting,
PR(41), No. 4, April 2008, pp. 1373-1383.
Elsevier DOI 0801
Linear discriminant analysis; Kernel methods; Feature extraction BibRef

Agrawal, R.K., Karmeshu,
Perturbation scheme for online learning of features: Incremental principal component analysis,
PR(41), No. 5, May 2008, pp. 1452-1460.
Elsevier DOI 0711
Statistical pattern recognition; Feature extraction; Face recognition; Principal component analysis; Variance-covariance matrix; Perturbation method BibRef

Hu, Y.F.[Ya-Feng], Lv, H.R.[Hai-Rong], Zhang, X.D.[Xian-Da],
Comments on 'An analytical algorithm for generalized low-rank approximations of matrices',
PR(41), No. 6, June 2008, pp. 2133-2135.
Elsevier DOI 0802

See also analytical algorithm for generalized low-rank approximations of matrices, An. Low rank approximation; Analytical algorithm; Iterative algorithm BibRef

Saegusa, R.[Ryo], Sakano, H.[Hitoshi], Hashimoto, S.[Shuji],
A Nonlinear Principal Component Analysis of Image Data,
IEICE(E88-D), No. 10, October 2005, pp. 2242-2248.
DOI Link 0510
BibRef

Pang, Y., Tao, D.C., Yuan, Y., Li, X.L.,
Binary Two-Dimensional PCA,
SMC-B(37), No. 4, August 2008, pp. 1176-1180.
IEEE DOI 0808
BibRef

Rahman, M.M.[M. Masudur], Ishikawa, S.[Seiji],
Overcoming Dress Effect In Eigenspace,
IJIG(5), No. 4, October 2005, pp. 811-823. 0510
BibRef

Wu, W., Ahmad, M.O., Samadi, S.,
Discriminant analysis based on modified generalised singular value decomposition and its numerical error analysis,
IET-CV(3), No. 3, September 2009, pp. 159-173.
DOI Link 0909
SVD to perform LDA. BibRef

Fowler, J.E.,
Compressive-Projection Principal Component Analysis,
IP(18), No. 10, October 2009, pp. 2230-2242.
IEEE DOI 0909
BibRef

Costache, G.N.[Gabriel Nicolae], Corcoran, P.[Peter], Puslecki, P.[Pawel],
Combining PCA-based datasets without retraining of the basis vector set,
PRL(30), No. 16, 1 December 2009, pp. 1441-1447.
Elsevier DOI 0911
Principal component analysis; Incremental PCA; Combining collections BibRef

Gurumoorthy, K.S.[Karthik S.], Rajwade, A.[Ajit], Banerjee, A.[Arunava], Rangarajan, A.[Anand],
A Method for Compact Image Representation Using Sparse Matrix and Tensor Projections Onto Exemplar Orthonormal Bases,
IP(19), No. 2, February 2010, pp. 322-334.
IEEE DOI 1002
BibRef
Earlier:
Beyond SVD: Sparse projections onto exemplar orthonormal bases for compact image representation,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Jenssen, R.[Robert],
Kernel Entropy Component Analysis,
PAMI(32), No. 5, May 2010, pp. 847-860.
IEEE DOI 1003
For data transformation and dimensionality reduction. BibRef

Jenssen, R.[Robert], Storås, O.[Ola],
Kernel Entropy Component Analysis Pre-images for Pattern Denoising,
SCIA09(626-635).
Springer DOI 0906
BibRef

Zafeiriou, S.P.[Stefanos P.], Petrou, M.[Maria],
Nonlinear Non-Negative Component Analysis Algorithms,
IP(19), No. 4, April 2010, pp. 1050-1066.
IEEE DOI 1003
BibRef
Earlier:
Nonlinear Nonnegative Component Analysis,
CVPR09(2860-2865).
IEEE DOI 0906
BibRef

Si, S., Tao, D., Chan, K.P.,
Evolutionary Cross-Domain Discriminative Hessian Eigenmaps,
IP(19), No. 4, April 2010, pp. 1075-1086.
IEEE DOI 1003
BibRef

Bergqvist, G., Larsson, E.G.,
The Higher-Order Singular Value Decomposition: Theory and an Application,
SPMag(27), No. 3, 2010, pp. 151-154.
IEEE DOI 1006
Survey, SVD. Lecture Notes BibRef

Li, X.L.[Xue-Long], Pang, Y.W.[Yan-Wei], Yuan, Y.[Yuan],
L1-Norm-Based 2DPCA,
SMC-B(40), No. 4, August 2010, pp. 1170-1175.
IEEE DOI 1008
BibRef

Mirkin, B.[Boris],
Core Concepts in Data Analysis: Summarization, Correlation and Visualization,
Springer2011, ISBN: 978-0-85729-286-5
WWW Link. Buy this book: Core Concepts in Data Analysis: Summarization, Correlation and Visualization (Undergraduate Topics in Computer Science) 1106
In depth analysis of summarization tools such as PCA. BibRef

Liang, Z.Z.[Zhi-Zheng], Xia, S.X.[Shi-Xiong], Zhou, Y.[Yong], Li, Y.F.[You-Fu],
Blockwise projection matrix versus blockwise data on undersampled problems: Analysis, comparison and applications,
PR(44), No. 10-11, October-November 2011, pp. 2774-2785.
Elsevier DOI 1101
LDA; PCA; Blockwise PCA; Blockwise LDA; 2DPCA; 2DLDA; Face recognition; Gene expression data BibRef

Ponce, C., Singer, A.,
Computing Steerable Principal Components of a Large Set of Images and Their Rotations,
IP(20), No. 11, November 2011, pp. 3051-3062.
IEEE DOI 1110
Applied to electron microscopy. BibRef

Zhang, W.T., Lou, S.T.,
A Low Complexity Iterative Algorithm for Joint Zero Diagonalization,
SPLetters(19), No. 2, February 2012, pp. 115-118.
IEEE DOI 1201
BibRef

Lee, K.Y.[Kwan-Yong], Park, H.[Hyeyoung],
Probabilistic learning of similarity measures for tensor PCA,
PRL(33), No. 10, 15 July 2012, pp. 1364-1372.
Elsevier DOI 1205
Tensor; Principal component analysis; Similarity measure; Probabilistic learning BibRef

Rusu, C., Dumitrescu, B.,
Stagewise K-SVD to Design Efficient Dictionaries for Sparse Representations,
SPLetters(19), No. 10, October 2012, pp. 631-634.
IEEE DOI 1209
BibRef

Li, J.[Jun], Tao, D.C.[Da-Cheng],
On Preserving Original Variables in Bayesian PCA With Application to Image Analysis,
IP(21), No. 12, December 2012, pp. 4830-4843.
IEEE DOI 1212
BibRef

Smith, L.N., Elad, M.,
Improving Dictionary Learning: Multiple Dictionary Updates and Coefficient Reuse,
SPLetters(20), No. 1, January 2013, pp. 79-82.
IEEE DOI 1212
BibRef

Liu, W., Lin, W.,
Additive White Gaussian Noise Level Estimation in SVD Domain for Images,
IP(22), No. 3, March 2013, pp. 872-883.
IEEE DOI 1302
BibRef

Hirokawa, M.[Mariko], Kuroki, Y.[Yoshimitsu],
A Fast Implementation of PCA-L1 Using Gram-Schmidt Orthogonalization,
IEICE(E96-D), No. 3, March 2013, pp. 559-561.
WWW Link. 1303
BibRef

Ghassabeh, Y.A.[Youness Aliyari], Abrishami Moghaddam, H.[Hamid],
Adaptive linear discriminant analysis for online feature extraction,
MVA(24), No. 4, May 2013, pp. 777-794.
Springer DOI 1304
computation of the square root of the inverse covariance matrix. BibRef

Van Nguyen, H., Patel, V.M., Nasrabadi, N.M., Chellappa, R.,
Design of Non-Linear Kernel Dictionaries for Object Recognition,
IP(22), No. 12, 2013, pp. 5123-5135.
IEEE DOI 1312
image classification BibRef

Shekhar, S., Patel, V.M., Van Nguyen, H., Chellappa, R.,
Coupled Projections for Adaptation of Dictionaries,
IP(24), No. 10, October 2015, pp. 2941-2954.
IEEE DOI 1507
Cost function BibRef

Bigot, J., Gouet, R., López, A.,
Geometric PCA of Images,
SIIMS(6), No. 4, 2013, pp. 1851-1879.
DOI Link 1402
BibRef

Vidal, R.[René], Favaro, P.[Paolo],
Low rank subspace clustering (LRSC),
PRL(43), No. 1, 2014, pp. 47-61.
Elsevier DOI 1404
Subspace clustering BibRef

Favaro, P.[Paolo], Vidal, R.[Rene], Ravichandran, A.[Avinash],
A closed form solution to robust subspace estimation and clustering,
CVPR11(1801-1807).
IEEE DOI 1106
BibRef

Patel, V.M.[Vishal M.], Nguyen, H.V.[Hien Van], Vidal, R.[Rene],
Latent Space Sparse Subspace Clustering,
ICCV13(225-232)
IEEE DOI 1403
Subspace clustering; dimension reduction; sparse optimization BibRef

Tsakiris, M.C.[Manolis C.], Vidal, R.[René],
Filtrated Algebraic Subspace Clustering,
SIIMS(10), No. 1, 2017, pp. 372-415.
DOI Link 1704
BibRef
Earlier:
Dual Principal Component Pursuit,
RSL-CV15(850-858)
IEEE DOI 1602
Computational modeling BibRef
And:
Filtrated Spectral Algebraic Subspace Clustering,
RSL-CV15(868-876)
IEEE DOI 1602
Clustering algorithms BibRef

Tsakiris, M.C.[Manolis C.], Vidal, R.[René],
Algebraic Clustering of Affine Subspaces,
PAMI(40), No. 2, February 2018, pp. 482-489.
IEEE DOI 1801
Clustering methods, Complexity theory, Geometry, Motion segmentation, Silicon, Algebraic subspace clustering, homogeneous coordinates BibRef

Nguyen, H.V.[Hien V.], Patel, V.M.[Vishal M.],
Max residual classifier,
WACV14(580-587)
IEEE DOI 1406
Dictionaries BibRef

Damon, J.N.[James N.], Marron, J.S.,
Backwards Principal Component Analysis and Principal Nested Relations,
JMIV(50), No. 1-2, September 2014, pp. 107-114.
Springer DOI 1408
BibRef

Lee, M.[Minsik], Choi, C.H.[Chong-Ho],
Incremental N-Mode SVD for Large-Scale Multilinear Generative Models,
IP(23), No. 10, October 2014, pp. 4255-4269.
IEEE DOI 1410
image processing BibRef

Zhao, J.H.[Jian-Hua],
Efficient Model Selection for Mixtures of Probabilistic PCA Via Hierarchical BIC,
Cyber(44), No. 10, October 2014, pp. 1871-1883.
IEEE DOI 1410
Bayes methods BibRef

Lin, M.[Ming], Wang, F.[Fei], Zhang, C.S.[Chang-Shui],
Large-scale eigenvector approximation via Hilbert Space Embedding Nyström,
PR(48), No. 5, 2015, pp. 1904-1912.
Elsevier DOI 1502
Eigenvalues and eigenfunctions BibRef

Ghassabeh, Y.A.[Youness Aliyari], Rudzicz, F.[Frank], Abrishami Moghaddam, H.[Hamid],
Fast incremental LDA feature extraction,
PR(48), No. 6, 2015, pp. 1999-2012.
Elsevier DOI 1503
Incremental linear discriminant analysis BibRef

Lin, G.Y.[Guan-You], Tang, N.Z.[Nian-Zu], Wang, H.X.[Hai-Xian],
Locally principal component analysis based on L1-norm maximisation,
IET-IPR(9), No. 2, 2015, pp. 91-96.
DOI Link 1503
data handling BibRef

Hintermüller, M.[Michael], Wu, T.[Tao],
Robust Principal Component Pursuit via Inexact Alternating Minimization on Matrix Manifolds,
JMIV(51), No. 3, March 2015, pp. 361-377.
WWW Link. 1504
BibRef

Wang, R., Nie, F., Yang, X., Gao, F., Yao, M.,
Robust 2DPCA With Non-greedy L_1-Norm Maximization for Image Analysis,
Cyber(45), No. 5, May 2015, pp. 1108-1112. 1505
Databases BibRef

Ju, F.[Fujiao], Sun, Y.F.[Yan-Feng], Gao, J.B.[Jun-Bin], Hu, Y.L.[Yong-Li], Yin, B.C.[Bao-Cai],
Image Outlier Detection and Feature Extraction via L1-Norm-Based 2D Probabilistic PCA,
IP(24), No. 12, December 2015, pp. 4834-4846.
IEEE DOI 1512
Bayes methods BibRef

Yadav, S.K., Sinha, R., Bora, P.K.,
An Efficient SVD Shrinkage for Rank Estimation,
SPLetters(22), No. 12, December 2015, pp. 2406-2410.
IEEE DOI 1512
estimation theory BibRef

Wang, J.,
Generalized 2-D Principal Component Analysis by Lp-Norm for Image Analysis,
Cyber(46), No. 3, March 2016, pp. 792-803.
IEEE DOI 1602
Algorithm design and analysis BibRef

Oh, T.H.[Tae-Hyun], Tai, Y.W.[Yu-Wing], Bazin, J.C.[Jean-Charles], Kim, H.W.[Hyeong-Woo], Kweon, I.S.[In So],
Partial Sum Minimization of Singular Values in Robust PCA: Algorithm and Applications,
PAMI(38), No. 4, April 2016, pp. 744-758.
IEEE DOI 1603
BibRef
Earlier: A1, A4, A2, A3, A5:
Partial Sum Minimization of Singular Values in RPCA for Low-Level Vision,
ICCV13(145-152)
IEEE DOI 1403
Approximation methods. Nuclear Norm BibRef

Oh, T.H.[Tae-Hyun], Matsushita, Y.[Yasuyuki], Tai, Y.W.[Yu-Wing], Kweon, I.S.[In So],
Fast Randomized Singular Value Thresholding for Low-Rank Optimization,
PAMI(40), No. 2, February 2018, pp. 376-391.
IEEE DOI 1801
BibRef
Earlier:
Fast randomized Singular Value Thresholding for Nuclear Norm Minimization,
CVPR15(4484-4493)
IEEE DOI 1510
Acceleration, Complexity theory, Matrix decomposition, Minimization, Optimization, Sparse matrices, robust principal component analysis BibRef

He, X.F.[Xiao-Fei], Zhang, C.Y.[Chi-Yuan], Zhang, L.J.[Li-Jun], Li, X.L.[Xue-Long],
A-Optimal Projection for Image Representation,
PAMI(38), No. 5, May 2016, pp. 1009-1015.
IEEE DOI 1604
design of experiments BibRef

Wang, J.H.[Jian-Hong], Zhang, P.Z.[Pin-Zheng], Luo, L.M.[Lin-Min],
Nonnegative Component Representation with Hierarchical Dictionary Learning Strategy for Action Recognition,
IEICE(E99-D), No. 4, April 2016, pp. 1259-1263.
WWW Link. 1604
mid-level representation based on nonnegative matrix factorization. BibRef

Keeling, S.L.[Stephen L.], Kunisch, K.[Karl],
Robust L1 Approaches to Computing the Geometric Median and Principal and Independent Components,
JMIV(56), No. 1, September 2016, pp. 99-124.
WWW Link. 1605
BibRef

da Silva, A.P., Comon, P., de Almeida, A.L.F.,
A Finite Algorithm to Compute Rank-1 Tensor Approximations,
SPLetters(23), No. 7, July 2016, pp. 959-963.
IEEE DOI 1608
singular value decomposition BibRef

Tichavský, P., Phan, A.H., Cichocki, A.,
Partitioned Alternating Least Squares Technique for Canonical Polyadic Tensor Decomposition,
SPLetters(23), No. 7, July 2016, pp. 993-997.
IEEE DOI 1608
convergence of numerical methods BibRef

Xing, H.J.[Hong-Jie], Wang, X.Z.[Xi-Zhao],
Selective ensemble of SVDDs with Renyi entropy based diversity measure,
PR(61), No. 1, 2017, pp. 185-196.
Elsevier DOI 1705
One-class classification BibRef

Kviatkovsky, I., Gabel, M., Rivlin, E., Shimshoni, I.[Ilan],
On the Equivalence of the LC-KSVD and the D-KSVD Algorithms,
PAMI(39), No. 2, February 2017, pp. 411-416.
IEEE DOI 1702
Algorithm design and analysis BibRef

Liu, Y., Gao, Q., Miao, S., Gao, X., Nie, F., Li, Y.,
A Non-Greedy Algorithm for L1-Norm LDA,
IP(26), No. 2, February 2017, pp. 684-695.
IEEE DOI 1702
face recognition BibRef

Dumitrescu, B., Irofti, P.,
Regularized K-SVD,
SPLetters(24), No. 3, March 2017, pp. 309-313.
IEEE DOI 1702
Approximation algorithms BibRef

Wang, Q.Q.[Qian-Qian], Gao, Q.X.[Quan-Xue], Gao, X.B.[Xin-Bo], Nie, F.P.[Fei-Ping],
Optimal mean two-dimensional principal component analysis with F-norm minimization,
PR(68), No. 1, 2017, pp. 286-294.
Elsevier DOI 1704
Dimensionality reduction BibRef

Ye, Q., Zhao, H., Fu, L., Gao, S.,
Underlying Connections Between Algorithms for Nongreedy LDA-L1,
IP(27), No. 5, May 2018, pp. 2557-2559.
IEEE DOI 1804
Algorithm design and analysis, Indexes, Internet of Things, Linear discriminant analysis, Mobile communication, Upper bound, improved L1-norm linear discriminant analysis (ILDA-L1) BibRef

Gao, Q., Ma, L., Liu, Y., Gao, X., Nie, F.,
Angle 2DPCA: A New Formulation for 2DPCA,
Cyber(48), No. 5, May 2018, pp. 1672-1678.
IEEE DOI 1804
Covariance matrices, Feature extraction, Image reconstruction, Linear programming, Measurement, Principal component analysis, dimensionality reduction BibRef

Silva, D.G., Attux, R.,
Simulated Annealing for Independent Component Analysis Over Galois Fields,
SPLetters(25), No. 4, April 2018, pp. 516-520.
IEEE DOI 1804
Galois fields, combinatorial mathematics, computational complexity, entropy, finite fields BibRef

Miao, J., Cheng, G., Cai, Y., Xia, J.,
Approximate Joint Singular Value Decomposition Algorithm Based on Givens-Like Rotation,
SPLetters(25), No. 5, May 2018, pp. 620-624.
IEEE DOI 1805
Hermitian matrices, approximation theory, blind source separation, matrix algebra, joint singular value decomposition BibRef

Shang, F.H.[Fan-Hua], Cheng, J.[James], Liu, Y.Y.[Yuan-Yuan], Luo, Z.Q.[Zhi-Quan], Lin, Z.C.[Zhou-Chen],
Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications,
PAMI(40), No. 9, September 2018, pp. 2066-2080.
IEEE DOI 1808
Minimization, Sparse matrices, Robustness, Principal component analysis, Algorithm design and analysis, alternating direction method of multipliers (ADMM) BibRef

Smallman, L.[Luke], Artemiou, A.[Andreas], Morgan, J.[Jennifer],
Sparse Generalised Principal Component Analysis,
PR(83), 2018, pp. 443-455.
Elsevier DOI 1808
Dimension reduction, PCA, Text mining, Exponential family BibRef

Vaswani, N., Chi, Y., Bouwmans, T.,
Rethinking PCA for Modern Data Sets: Theory, Algorithms, and Applications,
PIEEE(106), No. 8, August 2018, pp. 1274-1276.
IEEE DOI 1808
Special issues and sections, Principal component analysis, Statistical analysis, Algorithm design and analysis BibRef

Johnstone, I.M., Paul, D.,
PCA in High Dimensions: An Orientation,
PIEEE(106), No. 8, August 2018, pp. 1277-1292.
IEEE DOI 1808
Eigenvalues and eigenfunctions, Covariance matrices, Principal component analysis, Statistical analysis, Estimation, Tracy-Widom law BibRef

Balzano, L., Chi, Y., Lu, Y.M.,
Streaming PCA and Subspace Tracking: The Missing Data Case,
PIEEE(106), No. 8, August 2018, pp. 1293-1310.
IEEE DOI 1808
Principal component analysis, Signal processing algorithms, Signal processing, Radar tracking, Statistical analysis, subspace and low-rank models BibRef

Zou, H., Xue, L.,
A Selective Overview of Sparse Principal Component Analysis,
PIEEE(106), No. 8, August 2018, pp. 1311-1320.
IEEE DOI 1808
Principal component analysis, Statistical analysis, Covariance matrices, Dimensionality reduction, Sparse matrices, statistical learning BibRef

Wu, S.X., Wai, H., Li, L., Scaglione, A.,
A Review of Distributed Algorithms for Principal Component Analysis,
PIEEE(106), No. 8, August 2018, pp. 1321-1340.
IEEE DOI 1808
Principal component analysis, Signal processing algorithms, Distributed databases, Statistical analysis, radar signal processing BibRef

Zare, A., Ozdemir, A., Iwen, M.A., Aviyente, S.,
Extension of PCA to Higher Order Data Structures: An Introduction to Tensors, Tensor Decompositions, and Tensor PCA,
PIEEE(106), No. 8, August 2018, pp. 1341-1358.
IEEE DOI 1808
Tensile stress, Principal component analysis, Dimensionality reduction, Matrix decomposition, tensor PCA BibRef

Vaswani, N., Narayanamurthy, P.,
Static and Dynamic Robust PCA and Matrix Completion: A Review,
PIEEE(106), No. 8, August 2018, pp. 1359-1379.
IEEE DOI 1808
Principal component analysis, Statistical analysis, Sparse matrices, Matrix decomposition, Dimensionality reduction, robust subspace tracking BibRef

Lerman, G., Maunu, T.,
An Overview of Robust Subspace Recovery,
PIEEE(106), No. 8, August 2018, pp. 1380-1410.
IEEE DOI 1808
Robustness, Principal component analysis, Data models, Statistical analysis, Analytical models, Matrix decomposition, Unsupervised learning BibRef

Ma, S., Aybat, N.S.,
Efficient Optimization Algorithms for Robust Principal Component Analysis and Its Variants,
PIEEE(106), No. 8, August 2018, pp. 1411-1426.
IEEE DOI 1808
Principal component analysis, Sparse matrices, Robustness, Optimization, Convergence, Probability, Statistical analysis, ? -stationary solution BibRef

Bouwmans, T., Javed, S., Zhang, H., Lin, Z., Otazo, R.,
On the Applications of Robust PCA in Image and Video Processing,
PIEEE(106), No. 8, August 2018, pp. 1427-1457.
IEEE DOI 1808
Robustness, Principal component analysis, Statistical analysis, Sparse matrices, Image processing, 3-D computer vision BibRef

Yazdi, S.V.[Saeed Varasteh], Douzal-Chouakria, A.[Ahlame],
Time warp invariant kSVD: Sparse coding and dictionary learning for time series under time warp,
PRL(112), 2018, pp. 1-8.
Elsevier DOI 1809
SVD, Sparse coding, Dictionary learning, Time series, Time warping BibRef

Schnass, K.,
Average Performance of Orthogonal Matching Pursuit (OMP) for Sparse Approximation,
SPLetters(25), No. 12, December 2018, pp. 1865-1869.
IEEE DOI 1812
BibRef
And: Corrections: SPLetters(26), No. 10, October 2019, pp. 1566-1567.
IEEE DOI 1909
approximation theory, iterative methods, signal processing, time-frequency analysis, orthogonal matching pursuit, OMP, decaying coefficients BibRef

Tarzanagh, D.A.[Davoud Ataee], Michailidis, G.[George],
Fast Randomized Algorithms for t-Product Based Tensor Operations and Decompositions with Applications to Imaging Data,
SIIMS(11), No. 4, 2018, pp. 2629-2664.
DOI Link 1901
BibRef

Hovhannisyan, V.[Vahan], Panagakis, Y.[Yannis], Parpas, P.[Panos], Zafeiriou, S.P.[Stefanos P.],
Fast Multilevel Algorithms for Compressive Principal Component Pursuit,
SIIMS(12), No. 1, 2019, pp. 624-649.
DOI Link 1904
BibRef
Earlier:
Multilevel Approximate Robust Principal Component Analysis,
Matrix-Tensor17(536-544)
IEEE DOI 1802
Approximation algorithms, Computational modeling, Matrix decomposition, Optimization, Principal component analysis, Sparse matrices BibRef

Mo, D.M.[Dong-Mei], Lai, Z.H.[Zhi-Hui], Wong, W.K.[Wai-Keung],
Locally Joint Sparse Marginal Embedding for Feature Extraction,
MultMed(21), No. 12, December 2019, pp. 3038-3052.
IEEE DOI 1912
Code:
WWW Link. Feature extraction, Sparse matrices, Linear discriminant analysis, Principal component analysis, robustness BibRef

Machidon, A.L.[Alina L.], Machidon, O.M.[Octavian M.], Ciobanu, C.B.[Catalin B.], Ogrutan, P.L.[Petre L.],
Accelerating a Geometrical Approximated PCA Algorithm Using AVX2 and CUDA,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Kim, C.[Cheolmin], Klabjan, D.[Diego],
A Simple and Fast Algorithm for L1-Norm Kernel PCA,
PAMI(42), No. 8, August 2020, pp. 1842-1855.
IEEE DOI 2007
Principal component analysis, Kernel, Matrix decomposition, Convergence, Anomaly detection, Loading, Sparse matrices, outlier detection BibRef

Rontogiannis, A.A., Giampouras, P.V., Koutroumbas, K.D.,
Online Reweighted Least Squares Robust PCA,
SPLetters(27), 2020, pp. 1340-1344.
IEEE DOI 2008
Sparse matrices, Signal processing algorithms, Minimization, Linear programming, Matrix decomposition, Robustness, robust PCA BibRef

Rusu, C.[Cristian],
An Iterative Coordinate Descent Algorithm to Compute Sparse Low-Rank Approximations,
SPLetters(29), 2022, pp. 249-253.
IEEE DOI 2202
Signal processing algorithms, Optimization, Approximation algorithms, Principal component analysis, low-rank approximation BibRef

Salloum, R.[Ronald], Kuo, C.-.C.J.[C.-C. Jay],
cPCA++: An efficient method for contrastive feature learning,
PR(124), 2022, pp. 108378.
Elsevier DOI 2203
PCA, Contrastive PCA, Feature learning, Dimensionality reduction BibRef

Wang, W.[Wei], Dang, Z.[Zheng], Hu, Y.L.[Yin-Lin], Fua, P.[Pascal], Salzmann, M.[Mathieu],
Robust Differentiable SVD,
PAMI(44), No. 9, September 2022, pp. 5472-5487.
IEEE DOI 2208
Covariance matrices, Eigenvalues and eigenfunctions, Training, Explosions, Decorrelation, Taylor series, taylor expansion BibRef

Song, Y.[Yue], Sebe, N.[Nicu], Wang, W.[Wei],
Orthogonal SVD Covariance Conditioning and Latent Disentanglement,
PAMI(45), No. 7, July 2023, pp. 8773-8786.
IEEE DOI 2306
BibRef
Earlier:
Improving Covariance Conditioning of the SVD Meta-layer by Orthogonality,
ECCV22(XXIV:356-372).
Springer DOI 2211
Covariance matrices, Training, Decorrelation, Matrix decomposition, Task analysis, Eigenvalues and eigenfunctions, Neural networks, unsupervised latent disentanglement BibRef

Moon, M.[Minam], Hur, I.[Injo], Moon, S.[Sunghwan],
Singular Value Decomposition of the Wave Forward Operator with Radial Variable Coefficients,
SIIMS(16), No. 3, 2023, pp. 1520-1534.
DOI Link 2309
BibRef

Arashloo, S.R.[Shervin Rahimzadeh],
Large-margin multiple kernel Lp-SVDD using Frank-Wolfe algorithm for novelty detection,
PR(148), 2024, pp. 110189.
Elsevier DOI 2402
L-SVDD, Large-margin learning, Convex optimisation, Frank-Wolfe algorithm, Multiple kernel learning, Novelty detection BibRef

Geng, X.Y.[Xiao-Yu], Guo, Q.[Qiang], Hui, S.[Shuaixiong], Yang, M.[Ming], Zhang, C.M.[Cai-Ming],
Tensor robust PCA with nonconvex and nonlocal regularization,
CVIU(243), 2024, pp. 104007.
Elsevier DOI Code:
WWW Link. 2405
Low-rank property, Nonconvex surrogate, Nonlocal self-similarity, Tensor robust PCA BibRef

Ghosh, T.[Tomojit], Kirby, M.[Michael],
Linear Centroid Encoder for Supervised Principal Component Analysis,
PR(155), 2024, pp. 110634.
Elsevier DOI 2408
Supervised Linear Centroid-Encoder, Centroid-Encoder, Principal component analysis (PCA), Supervised PCA, Supervised dimensionality reduction BibRef

Rajpurohit, P.[Pushpendra], Arora, A.[Aakash], Babu, P.[Prabhu],
A Block Minorization-Maximization Algorithm for Row-Sparse Principal Component Analysis,
SPLetters(31), 2024, pp. 1905-1909.
IEEE DOI 2408
Covariance matrices, Sparse matrices, Principal component analysis, Signal processing algorithms, sparse principal component analysis BibRef

Fang, S.[Shun], Xu, Z.Q.[Zheng-Qin], Wu, S.Q.[Shi-Qian], Xie, S.L.[Shou-Lie],
Efficient Robust Principal Component Analysis via Block Krylov Iteration and CUR Decomposition,
CVPR23(1348-1357)
IEEE DOI 2309
BibRef

Ozdemir, C.[Cagri], Hoover, R.C.[Randy C.], Caudle, K.[Kyle],
2DTPCA: A New Framework for Multilinear Principal Component Analysis,
ICIP21(344-348)
IEEE DOI 2201
Tensors, Image recognition, Face recognition, Principal component analysis, Singular value decomposition, tensor singular value decomposition BibRef

Cai, H.Q.[Han-Qin], Chao, Z.[Zehan], Huang, L.X.[Long-Xiu], Needell, D.[Deanna],
Fast Robust Tensor Principal Component Analysis via Fiber CUR Decomposition,
RSLCV21(189-197)
IEEE DOI 2112
Tensors, Color, Computational complexity, Principal component analysis BibRef

Zhang, M., Gao, Y., Sun, C., Blumenstein, M.,
Kernel Mean P Power Error Loss for Robust Two-Dimensional Singular Value Decomposition,
ICIP19(3432-3436)
IEEE DOI 1910
2DSVD, correntropy, non-second order minimization, image clustering BibRef

Tsingalis, I., Kotropoulos, C.,
A Simple Algorithm for Non-Negative Sparse Principal Component Analysis,
ICIP19(2075-2079)
IEEE DOI 1910
sparse decompositions, non-negativity, PCA, subspace learning, eigenvectors, eigenvalues, Oja's rule BibRef

Xu, S., Zhang, X., Liao, S.,
A Linear Incremental Nyström Method for Online Kernel Learning,
ICPR18(2256-2261)
IEEE DOI 1812
Kernel, Matrix decomposition, Time complexity, Singular value decomposition, Approximation algorithms, Approximation methods BibRef

Chen, G.,
Scalable spectral clustering with cosine similarity,
ICPR18(314-319)
IEEE DOI 1812
matrix algebra, pattern clustering, singular value decomposition, scalable spectral clustering, cosine similarity, Laplace equations BibRef

Zhang, Z., Jiang, W., Li, S., Qin, J., Liu, G., Yan, S.,
Robust Locality-Constrained Label Consistent K-SVD by Joint Sparse Embedding,
ICPR18(1664-1669)
IEEE DOI 1812
Dictionaries, Sparse matrices, Machine learning, Training data, Laplace equations, Noise reduction, Encoding, classification BibRef

Shen, M., Wang, R.,
A New Singular Value Decomposition Algorithm for Octonion Signal,
ICPR18(3233-3237)
IEEE DOI 1812
Noise reduction, Singular value decomposition, Matrix decomposition, Image reconstruction, Quaternions, Image Denoising BibRef

Fronckova, K.[Katerina], Prazak, P.[Pavel], Slaby, A.[Antonin],
Singular Value Decomposition in Image Compression and Blurred Image Restoration,
ICIAR18(62-67).
Springer DOI 1807
BibRef

Chen, H., Sun, Y., Gao, J., Hu, Y., Ju, F.,
L1-2DPCA Revisit via Optimization on Product Manifolds,
DICTA17(1-7)
IEEE DOI 1804
greedy algorithms, image classification, image reconstruction, optimisation, principal component analysis, EM algorithm, BibRef

Erichson, N.B., Brunton, S.L., Kutz, J.N.,
Compressed Singular Value Decomposition for Image and Video Processing,
RSL-CV17(1880-1888)
IEEE DOI 1802
Approximation algorithms, Compressed sensing, Eigenvalues and eigenfunctions, Image coding, Sparse matrices BibRef

Paradkar, M.[Mihir], Udell, M.[Madeleine],
Graph-Regularized Generalized Low-Rank Models,
Tensor17(1921-1926)
IEEE DOI 1709
GLRM. Convergence, Laplace equations, Linear programming, Principal component analysis, Robustness, Sparse matrices BibRef

Mao, M.[Minqi], Zheng, Z.L.[Zhong-Long], Chen, Z.Y.[Zhong-Yu], Liu, H.W.[Hua-Wen], He, X.W.[Xiao-Wei], Ye, R.H.[Rong-Hua],
Two-dimensional PCA hashing and its extension,
ICPR16(1624-1629)
IEEE DOI 1705
Binary codes, Covariance matrices, Encoding, Feature extraction, Principal component analysis, Quantization (signal), Training, Hashing, ITQ, PCAH, Two, dimension BibRef

Shah, S.[Sohil], Goldstein, T.[Tom], Studer, C.[Christoph],
Estimating Sparse Signals with Smooth Support via Convex Programming and Block Sparsity,
CVPR16(5906-5915)
IEEE DOI 1612
BibRef

Tan, M.K.[Ming-Kui], Xiao, S.J.[Shi-Jie], Gao, J.B.[Jun-Bin], Xu, D.[Dong], van den Hengel, A.J.[Anton J.], Shi, Q.F.[Qin-Feng],
Proximal Riemannian Pursuit for Large-Scale Trace-Norm Minimization,
CVPR16(5877-5886)
IEEE DOI 1612
BibRef

Lu, J.H.[Jian-Hua],
Robust two-dimensional principal component analysis via alternating optimization,
ICIP13(340-344)
IEEE DOI 1402
Covariance matrices BibRef

Li, L.[Lai], Liu, G.C.[Guang-Can], Liu, Q.S.[Qing-Shan],
Advancing Iterative Quantization Hashing Using Isotropic Prior,
MMMod16(II: 174-184).
Springer DOI 1601
BibRef

Gerardo de la Fraga, L.,
A very fast procedure to calculate the smallest singular value,
ICAPR15(1-4)
IEEE DOI 1511
computer vision BibRef

Azimi-Sadjadi, M.R.[Mahmood R.], Kopacz, J.[Justin], Klausner, N.[Nick],
K-SVD dictionary learning using a fast OMP with applications,
ICIP14(1599-1603)
IEEE DOI 1502
Detectors BibRef

Goncalves, H.[Hugo], Correia, M.[Miguel], Li, X.[Xin], Sankaranarayanan, A.[Aswin], Tavares, V.[Vitor],
DALM-SVD: Accelerated sparse coding through singular value decomposition of the dictionary,
ICIP14(4907-4911)
IEEE DOI 1502
Convergence BibRef

Thongkamwitoon, T.[Thirapiroon], Muammar, H.[Hani], Dragotti, P.L.[Pier Luigi],
Robust image recapture detection using a K-SVD learning approach to train dictionaries of edge profiles,
ICIP14(5317-5321)
IEEE DOI 1502
Cameras BibRef

Podosinnikova, A.[Anastasia], Setzer, S.[Simon], Hein, M.[Matthias],
Robust PCA: Optimization of the Robust Reconstruction Error Over the Stiefel Manifold,
GCPR14(121-131).
Springer DOI 1411
BibRef

Kim, H.W.J.[Hyun-Woo J.], Bendlin, B.B.[Barbara B.], Adluru, N.[Nagesh], Collins, M.D.[Maxwell D.], Chung, M.K.[Moo K.], Johnson, S.C.[Sterling C.], Davidson, R.J.[Richard J.], Singh, V.[Vikas],
Multivariate General Linear Models (MGLM) on Riemannian Manifolds with Applications to Statistical Analysis of Diffusion Weighted Images,
CVPR14(2705-2712)
IEEE DOI 1409
Code, MGLM.
WWW Link. Multivariate general linear models BibRef

Yamauchi, Y., Kanade, T., Fujiyoshi, H.,
Classifier Introducing Transition Likelihood Model Based on Quantization Residual,
ACPR13(272-277)
IEEE DOI 1408
binary codes BibRef

Zhang, F.L.[Fan-Long], Qian, J.J.[Jian-Jun], Yang, J.[Jian],
Nuclear Norm Based 2DPCA,
ACPR13(74-78)
IEEE DOI 1408
face recognition BibRef

Chen, Y.J.[Yu-Jin], Nießner, M.[Matthias], Dai, A.[Angela],
4DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understanding,
ECCV22(XXXII:543-560).
Springer DOI 2211
BibRef

Hou, J.[Ji], Graham, B.[Benjamin], Nießner, M.[Matthias], Xie, S.N.[Sai-Ning],
Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts,
CVPR21(15582-15592)
IEEE DOI 2111
Annotations, Semantics, Training data, Benchmark testing BibRef

Xie, S.N.[Sai-Ning], Feng, J.S.[Jia-Shi], Yan, S.C.[Shui-Cheng], Lu, H.T.[Hong-Tao],
Perception Preserving Projections,
BMVC13(xx-yy).
DOI Link 1402
BibRef

Jiang, B.[Bo], Ding, C.[Chris], Luo, B.[Bio], Tang, J.[Jin],
Graph-Laplacian PCA: Closed-Form Solution and Robustness,
CVPR13(3492-3498)
IEEE DOI 1309
Laplacian; PCA; graph; robustness BibRef

Bassu, D., Izmailov, R., McIntosh, A., Ness, L., Shallcross, D.,
Centralized multi-scale singular value decomposition for feature construction in LIDAR image classification problems,
AIPR12(1-6)
IEEE DOI 1307
computational geometry BibRef

Kimura, A.[Akisato], Sakano, H.[Hitoshi], Kameoka, H.[Hirokazu], Sugiyama, M.[Masashi],
Designing various component analysis at will,
ICPR12(2959-2962).
WWW Link. 1302
Generic Component Analysis (PCA, ICA, ...) BibRef

Hidaka, A.[Akinori], Kurita, T.[Takio],
Nonlinear Discriminant Analysis Based on Probability Estimation by Gaussian Mixture Model,
SSSPR14(133-142).
Springer DOI 1408
BibRef
Earlier:
Sparse Discriminant Analysis Based on the Bayesian Posterior Probability Obtained by L1 Regression,
SSSPR12(648-656).
Springer DOI 1211
BibRef

Li, F.X.[Fu-Xin], Lebanon, G.[Guy], Sminchisescu, C.[Cristian],
Chebyshev approximations to the histogram X^2 kernel,
CVPR12(2424-2431).
IEEE DOI 1208
BibRef

Yang, Y.Q.[Yi-Qing], Zhang, L.[Li], Wang, S.[Sen], Jiang, H.R.[Hong-Rui], Murphy, C.J.[Chris J.], Hoeve, J.V.[Jim Ver],
A multi-affine model for tensor decomposition,
ITCVPR11(1348-1355).
IEEE DOI 1201
SVD for tensor decomposition. BibRef

Li, H.Y.[Hong-Yu], Zhang, L.[Lin],
Dynamic Subspace Update with Incremental Nyström Approximation,
Subspace10(384-393).
Springer DOI 1109
for eigen decomposition BibRef

Schmidt, F.R.[Frank R.], Ackermann, H.[Hanno], Rosenhahn, B.[Bodo],
Multilinear Model Estimation with L2-Regularization,
DAGM11(81-90).
Springer DOI 1109
BibRef

Xu, W.P.[Wei-Ping], Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.],
Determining the Cause of Negative Dissimilarity Eigenvalues,
CAIP11(I: 589-597).
Springer DOI 1109
BibRef

Mu, Y.D.[Ya-Dong], Dong, J.[Jian], Yuan, X.T.[Xiao-Tong], Yan, S.C.[Shui-Cheng],
Accelerated low-rank visual recovery by random projection,
CVPR11(2609-2616).
IEEE DOI 1106
computing the R-PCA efficiently BibRef

Abrams, A.[Austin], Feder, E.[Emily], Pless, R.[Robert],
Exploratory analysis of time-lapse imagery with fast subset PCA,
WACV11(336-343).
IEEE DOI 1101
Quickly compute PCA on a subset of the date (spatial and temporal subsets). BibRef

Kwatra, V.[Vivek], Han, M.[Mei],
Fast Covariance Computation and Dimensionality Reduction for Sub-window Features in Images,
ECCV10(II: 156-169).
Springer DOI 1009
BibRef

Chen, W.[Wei], Huang, K.Q.[Kai-Qi], Tan, T.N.[Tie-Niu], Tao, D.C.[Da-Cheng],
A convergent solution to two dimensional linear discriminant analysis,
ICIP09(4133-4136).
IEEE DOI 0911
BibRef

Feng, J.Z.[Jian-Zhou], Song, L.[Li], Yang, X.K.[Xiao-Kang], Zhang, W.J.[Wen-Jun],
Learning dictionary via subspace segmentation for sparse representation,
ICIP11(1245-1248).
IEEE DOI 1201
BibRef
Earlier:
Sub clustering K-SVD: Size variable dictionary learning for sparse representations,
ICIP09(2149-2152).
IEEE DOI 0911
BibRef

Cheng, P.[Peng], Li, W.Q.[Wan-Qing], Ogunbona, P.[Philip],
Greedy Approximation of Kernel PCA by Minimizing the Mapping Error,
DICTA09(303-308).
IEEE DOI 0912
BibRef

Kong, X.Y.[Xiang-Yu], Hu, C.H.[Chang-Hua], Han, C.Z.[Chong-Zhao],
The Performance Analysis of the Self-Stabilizing Douglas's MCA Algorithm,
CISP09(1-5).
IEEE DOI 0910
smallest eigenvalue of the correlation matrix of input data. BibRef

Yang, Z.R.[Zhi-Rong], Laaksonen, J.T.[Jorma T.],
Informative Laplacian Projection,
SCIA09(359-368).
Springer DOI 0906
constructing the similarity matrix for eigendecomposition BibRef

Olsson, C.[Carl], Oskarsson, M.[Magnus],
A Convex Approach to Low Rank Matrix Approximation with Missing Data,
SCIA09(301-309).
Springer DOI 0906
Formulate problems as minimization problem to solve using SVD, which does not work well with missing data. BibRef

Mauthner, T.[Thomas], Kluckner, S.[Stefan], Roth, P.M.[Peter M.], Bischof, H.[Horst],
Efficient Object Detection Using Orthogonal NMF Descriptor Hierarchies,
DAGM10(212-221).
Springer DOI 1009
NMF: Non-negative Matrix Factorizations BibRef

Storer, M.[Markus], Roth, P.M.[Peter M.], Urschler, M.[Martin], Bischof, H.[Horst],
Fast-Robust PCA,
SCIA09(430-439).
Springer DOI 0906
BibRef

Lucini, M.M.[María M.], Frery, A.C.[Alejandro C.],
Robust Principal Components for Hyperspectral Data Analysis,
ICIAR09(126-135).
Springer DOI 0907
BibRef

Mazhar, R.[Raazia], Gader, P.D.[Paul D.],
EK-SVD: Optimized dictionary design for sparse representations,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Nguyen, N.[Nam], Liu, W.Q.[Wan-Quan], Venkatesh, S.[Svetha],
Boosting performance for 2D Linear Discriminant Analysis via regression,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Andriyashin, A.[Alexey], Parkkinen, J.[Jussi], Jaaskelainen, T.[Timo],
Illuminant dependence of PCA, NMF and NTF in spectral color imaging,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Gai, J.D.[Jia-Ding], Li, Y.[Yong], Stevenson, R.L.[Robert L.],
Robust Bayesian PCA with Student's t-distribution: The variational inference approach,
ICIP08(1340-1343).
IEEE DOI 0810
BibRef
And:
An EM algorithm for robust Bayesian PCA with student's t-distribution,
ICIP08(2672-2675).
IEEE DOI 0810
BibRef

Thorstensen, N.[Nicolas], Keriven, R.[Renaud],
Non-rigid Shape Matching Using Geometry and Photometry,
ACCV09(III: 644-654).
Springer DOI 0909
BibRef

Thorstensen, N.[Nicolas], Segonne, F.[Florent], Keriven, R.[Renaud],
Pre-image as Karcher Mean Using Diffusion Maps: Application to Shape and Image Denoising,
SSVM09(721-732).
Springer DOI 0906
BibRef
Earlier:
Normalization and preimage problem in gaussian kernel PCA,
ICIP08(741-744).
IEEE DOI 0810
BibRef

Park, M.S.[Myoung Soo], Choi, J.Y.[Jin Young],
Novel Incremental Principal Component Analysis with Improved Performance,
SSPR08(592-601).
Springer DOI 0812
BibRef

Ding, C.[Chris], Huang, H.[Heng], Luo, D.[Dijun],
Tensor reduction error analysis: Applications to video compression and classification,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Ma, Y.[Yong], Ijiri, Y.[Yoshihisa], Lao, S.H.[Shi-Hong], Kawade, M.[Masato],
Re-weighting Linear Discrimination Analysis under ranking loss,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Shi, Y.G.[Yong-Gang], Lai, R.J.[Rong-Jie], Krishna, S.[Sheila], Sicotte, N.[Nancy], Dinov, I.D.[Ivo D.], Toga, A.W.[Arthur W.],
Anisotropic Laplace-Beltrami eigenmaps: Bridging Reeb graphs and skeletons,
MMBIA08(1-7).
IEEE DOI 0806
BibRef

di Martino, F.[Ferdinando], Loia, V.[Vincenzo], Sessa, S.[Salvatore],
A Fuzzy Hybrid Method for Image Decomposition Problem,
EvoIASP08(xx-yy).
Springer DOI 0804
BibRef

Takahashi, T.[Tomokazu], Lina, Ide, I.[Ichiro], Mekada, Y.[Yoshito], Murase, H.[Hiroshi],
Interpolation Between Eigenspaces Using Rotation in Multiple Dimensions,
ACCV07(II: 774-783).
Springer DOI 0711
Like rotation hyper-ellipsoid in high dimensional space. BibRef

Muñoz, A.[Alberto], González, J.[Javier],
Functional Learning of Kernels for Information Fusion Purposes,
CIARP08(277-283).
Springer DOI 0809
BibRef
Earlier:
Joint Diagonalization of Kernels for Information Fusion,
CIARP07(556-563).
Springer DOI 0711
BibRef

Lewis, J.P., Mostafavi, I.[Iman], Sosinsky, G.[Gina], Martone, M.E.[Maryanne E.], West, R.[Ruth],
Shape Priors by Kernel Density Modeling of PCA Residual Structure,
ICIP07(IV: 333-336).
IEEE DOI 0709
BibRef

Melenchón, J.[Javier], Martínez, E.[Elisa],
Efficiently Downdating, Composing and Splitting Singular Value Decompositions Preserving the Mean Information,
IbPRIA07(II: 436-443).
Springer DOI 0706
BibRef

Franc, V.[Vojtech], Hlavác, V.[Václav],
Greedy Kernel Principal Component Analysis,
CogVis03(87-105).
Springer DOI 0310
BibRef

Yan, S.C.[Shui-Cheng], Tang, X.[Xiaoou],
Trace Quotient Problems Revisited,
ECCV06(II: 232-244).
Springer DOI 0608
BibRef

Yan, S.C.[Shui-Cheng], Tang, X.[Xiaoou],
Largest-Eigenvalue-Theory for Incremental Principal Component Analysis,
ICIP05(I: 1181-1184).
IEEE DOI 0512
BibRef

Yang, A.Y.[Allen Y.], Rao, S.[Shankar], Wagner, A.[Andrew], Ma, Y.[Yi], Fossum, R.M.[Robert M.],
Hilbert Functions and Applications to the Estimation of Subspace Arrangements,
ICCV05(I: 158-165).
IEEE DOI 0510
e.g. factorization and eigen models. BibRef

Wang, H.C.[Hong-Cheng], Ahuja, N.[Narendra],
A Tensor Approximation Approach to Dimensionality Reduction,
IJCV(76), No. 3, March 2008, pp. 217-229.
Springer DOI 0801
BibRef
Earlier:
Rank-R Approximation of Tensors: Using Image-as-Matrix Representation,
CVPR05(II: 346-353).
IEEE DOI 0507
BibRef

Ghodsi, A.[Ali], Huang, J.Y.[Jia-Yuan], Southey, F.[Finnegan], Schuurmans, D.[Dale],
Tangent-Corrected Embedding,
CVPR05(I: 518-525).
IEEE DOI 0507
Use prior info from the sequence in PCA like methods. BibRef

Mühlich, M.[Matthias], Mester, R.[Rudolf],
Optimal Estimation of Homogeneous Vectors,
SCIA05(322-332).
Springer DOI 0506
BibRef

Mühlich, M.[Matthias], Mester, R.[Rudolf],
Unbiased Errors-In-Variables Estimation Using Generalized Eigensystem Analysis,
SMVP04(38-49).
Springer DOI 0505
BibRef

Yuan, X.T.[Xiao-Tong], Zhu, H.W.[Hong-Wen], Yang, S.T.[Shu-Tang],
A Robust Framework For Eigenspace Image Reconstruction,
WACV05(I: 54-59).
IEEE DOI 0502
Two step PCA. BibRef

Wilczkowiak, M., Sturm, P.F., Boyer, E.,
The Analysis of Ambiguous Solutions in Linear Systems and its Application to Computer Vision,
BMVC03(xx-yy).
HTML Version. 0409
Analysis of problems where degenerate cases are easy to detect. Based on SVD analysis. BibRef

Gribonval, R., Nielsen, M.,
Sparse decompositions in 'incoherent' dictionaries,
ICIP03(I: 33-36).
IEEE DOI 0312
BibRef

Tropp, J.A., Gilbert, A.C., Muthukrishnan, S., Strauss, M.J.,
Improved sparse approximation over quasi-incoherent dictionaries,
ICIP03(I: 37-40).
IEEE DOI 0312
BibRef

Rozell, C.[Christopher], Johnson, D.[Don], Baraniuk, R.G.[Richard G.], Olshausen, B.A.[Bruno A.],
Locally Competitive Algorithms for Sparse Approximation,
ICIP07(IV: 169-172).
IEEE DOI 0709
BibRef

Olshausen, B.A.,
Learning sparse, overcomplete representations of time-varying natural images,
ICIP03(I: 41-44).
IEEE DOI 0312
BibRef

Vermaak, J., Perez, P.,
Constrained subspace modelling,
CVPR03(II: 106-113).
IEEE DOI 0307
Probabilistic analysis of PCA. BibRef

Li, Y.W.[Yong-Win], Xu, L.Q.[Li-Qun], Morphett, J., Jacobs, R.,
An integrated algorithm of incremental and robust PCA,
ICIP03(I: 245-248).
IEEE DOI 0312
BibRef

Pei, S.C.[Soo-Chang], Chang, J.H.[Ja-Han], Ding, J.J.[Jian-Jiun],
Quaternion matrix singular value decomposition and its applications for color image processing,
ICIP03(I: 805-808).
IEEE DOI 0312
BibRef

Ho, J., Yang, M.H.[Ming-Hsuan], Lim, J.W.[Jong-Woo], Lee, K.C.[Kuang-Chih], Kriegman, D.J.,
Clustering appearances of objects under varying illumination conditions,
CVPR03(I: 11-18).
IEEE DOI 0307
BibRef

Fitzgibbon, A.W., Zisserman, A.,
Joint manifold distance: a new approach to appearance based clustering,
CVPR03(I: 26-33).
IEEE DOI 0307
BibRef

Levin, A.[Anat], Shashua, A.[Amnon],
Principal Component Analysis over Continuous Subspaces and Intersection of Half-Spaces,
ECCV02(III: 635 ff.).
Springer DOI 0205
BibRef

Pesquet-Popescu, B., Pesquet, J.C., Petropulu, A.P.,
Joint Singular Value Decomposition: A New Tool for Separable Representation of Images,
ICIP01(II: 569-572).
IEEE DOI 0108
BibRef

Brand, M.,
Incremental Singular Value Decomposition of Uncertain Data with Missing Values,
ECCV02(I: 707 ff.).
Springer DOI 0205
BibRef

Moghaddam, B., Zhou, X.[Xiang],
Factorized local appearance models,
ICPR02(III: 553-556).
IEEE DOI 0211
BibRef

Popovici, V., Thiran, J.P.,
PCA in autocorrelation space,
ICPR02(II: 132-135).
IEEE DOI 0211
BibRef

Chennubhotla, C., Jepson, A.D.,
Perceptual distance normalization for appearance detection,
ICPR04(II: 23-27).
IEEE DOI 0409
BibRef

Chennubhotla, C.[Chakra], Jepson, A.D.[Allan D.],
Sparse PCA: Extracting Multi-scale Structure from Data,
ICCV01(I: 641-647).
IEEE DOI 0106
BibRef

Iwamura, M., Omachi, S., Aso, H.,
A Modification of Eigenvalues to Compensate Estimation Errors of Eigenvectors,
ICPR00(Vol II: 378-381).
IEEE DOI 0009
BibRef

Wang, S., Xia, S.,
Self-Organizing Algorithm of Robust PCA Based on Single-Layer NN,
ICDAR97(851-854).
IEEE DOI 9708
BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Faces using Invariants, Appearence models -- Eigenfaces .