13.4.1.4 Invariants -- Principal Component Analysis

Pearson, K.[Karl],
On lines and planes of closest fit to systems of points in space,
Other JournalThe London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, Volume 2, No. 11, 1901, pp. 559-572.
DOI Link BibRef 0100

Jolliffe, I.T.,
Principal Component Analysis,
SpringerNew-York, 2002. ISBN: 978-0-387-95442-4.
WWW Link. Buy this book: Principal Component Analysis BibRef 0200
Earlier: First edition: Springer-VerlagNew-York, 1986. Survey, PCA. The Book, overview. BibRef

Shimura, M.[Masamichi], Imai, T.[Toshio],
Nonsupervised classification using the principal component,
PR(5), No. 4, December 1973, pp. 353-363.
Elsevier DOI 0309
BibRef

Wold, S.[Svante],
Pattern recognition by means of disjoint principal components models,
PR(8), No. 3, July 1976, pp. 127-139.
Elsevier DOI 0309
BibRef

Chang, C.C., Chen, Y.W., Buehrer, D.J.,
A Two-Dimensional Shape Recognition Scheme Based on Principal Component Analysis,
PRAI(8), 1994, pp. 859-875. BibRef 9400

Chen, C.Y., Chang, C.C., and Lee, R.C.T.,
A Near Pattern-Matching Scheme Based upon Principal Component Analysis,
PRL(16), 1995, pp. 339-345.
See also Exact Match Retrieval Scheme Based Upon Principal Component Analysis, An. BibRef 9500

Chang, C.I.[C. I], Du, Q.,
Interference and Noise-Adjusted Principal Components Analysis,
GeoRS(37), No. 5, September 1999, pp. 2387.
IEEE Top Reference. BibRef 9909

Weingessel, A.[Andreas], Bischof, H.[Horst], Hornik, K.[Kurt], Leisch, F.[Friedrich],
Adaptive Combination of PCA and VQ Networks,
TNN(8), No. 5, 1997, pp. 1208-1211. BibRef 9700
Earlier:
Hierarchies of Autoassociators,
ICPR96(IV: 200-204).
IEEE DOI 9608
(Technical Univ. of Vienna, A) BibRef

Crowley, J.L., Pourraz, F.,
Continuity properties of the appearance manifold for mobile robot position estimation,
IVC(19), No. 11, September 2001, pp. 741-752.
Elsevier DOI PCA. Orthogonal basis to represent appearance from different positions. 0108
BibRef

Twining, C.J., Taylor, C.J.,
The use of kernel principal component analysis to model data distributions,
PR(36), No. 1, January 2003, pp. 217-227.
Elsevier DOI 0210
BibRef

Glendinning, R.H., Herbert, R.A.,
Shape classification using smooth principal components,
PRL(24), No. 12, August 2003, pp. 2021-2030.
Elsevier DOI 0304
BibRef

de la Torre, F.[Fernando], Black, M.J.[Michael J.],
Robust Parameterized Component Analysis: Theory and Applications to 2D Facial Appearance Models,
CVIU(91), No. 1-2, July-August 2003, pp. 53-71.
Elsevier DOI
PDF File. 0309
BibRef
Earlier:
Robust Parameterized Component Analysis,
ECCV02(IV: 653 ff.).
Springer DOI 0205
BibRef
Earlier:
Robust Principal Component Analysis for Computer vision,
ICCV01(I: 362-369).
IEEE DOI 0106
BibRef
And:
Dynamic Coupled Component Analysis,
CVPR01(II:643-650).
IEEE DOI 0110
BibRef

de la Torre, F., Kanade, T.,
Oriented Discriminant Analysis,
BMVC04(xx-yy).
HTML Version. 0508
BibRef

Chen, Y.[Ying], de la Torre, F.[Fernando],
Active conditional models,
FG11(137-142).
IEEE DOI 1103
Faces with different pose and expression. ACM: combine features and ASM BibRef

Sengel, M., Bischof, H.,
Efficient representation of in-plane rotation within a PCA framework,
IVC(23), No. 12, 1 November 2005, pp. 1051-1059.
Elsevier DOI 0510
BibRef

Vidal, R., Ma, Y.[Yi], Sastry, S.,
Generalized Principal Component Analysis (GPCA),
PAMI(27), No. 12, December 2005, pp. 1945-1959.
IEEE DOI 0512
BibRef
Earlier: CVPR03(I: 621-628).
IEEE DOI 0307
BibRef

Vidal, R., Ma, Y.[Yi], Piazzi, J.,
A new GPCA algorithm for clustering subspaces by fitting, differentiating and dividing polynomials,
CVPR04(I: 510-517).
IEEE DOI 0408
BibRef

Nagabhushan, P., Guru, D.S., Shekar, B.H.,
Visual learning and recognition of 3D objects using two-dimensional principal component analysis: A robust and an efficient approach,
PR(39), No. 4, April 2006, pp. 721-725.
Elsevier DOI Principal component analysis; Appearance based model; Object recognition 0604
BibRef
Earlier: A3, A2, A1:
Object Recognition Through the Principal Component Analysis of Spatial Relationship Amongst Lines,
ACCV06(I:170-179).
Springer DOI 0601
BibRef

Shekar, B.H., Guru, D.S., Nagabhushan, P.,
Two-Dimensional Optimal Transform for Appearance Based Object Recognition,
ICCVGIP06(650-661).
Springer DOI 0612
BibRef

Irpino, A.[Antonio],
'Spaghetti' PCA analysis: An extension of principal components analysis to time dependent interval data,
PRL(27), No. 5, 1 April 2006, pp. 504-513.
Elsevier DOI 0604
Interval data; Time dependent; Oriented intervals BibRef

Sharma, A.[Alok], Paliwal, K.K.[Kuldip K.], Onwubolu, G.C.[Godfrey C.],
Class-dependent PCA, MDC and LDA: A combined classifier for pattern classification,
PR(39), No. 7, July 2006, pp. 1215-1229.
Elsevier DOI 0606
Classification accuracy; Total parameter requirement; Processing time; Class-dependent PCA; LDA BibRef

Sharma, A.[Alok], Paliwal, K.K.[Kuldip K.],
Subspace independent component analysis using vector kurtosis,
PR(39), No. 11, November 2006, pp. 2227-2232.
Elsevier DOI 0608
Blind source separation; Subspace ICA; Vector kurtosis BibRef

Sharma, A.[Alok], Paliwal, K.K.[Kuldip K.],
Fast principal component analysis using fixed-point algorithm,
PRL(28), No. 10, 15 July 2007, pp. 1151-1155.
Elsevier DOI 0706
Fast PCA; Eigenvalue decomposition; Mean squared error BibRef

Sharma, A.[Alok], Paliwal, K.K.[Kuldip K.],
A two-stage linear discriminant analysis for face-recognition,
PRL(33), No. 9, 1 July 2012, pp. 1157-1162.
Elsevier DOI 1202
Two-stage linear discriminant analysis; Small sample size problem; Classification accuracy
See also new perspective to null linear discriminant analysis method and its fast implementation using random matrix multiplication with scatter matrices, A. BibRef

Vaswani, N., Chellappa, R.,
Principal Components Null Space Analysis for Image and Video Classification,
IP(15), No. 7, July 2006, pp. 1816-1830.
IEEE DOI 0606
BibRef
Earlier:
Classification probability analysis of principal component null space analysis,
ICPR04(I: 240-243).
IEEE DOI 0409
BibRef

Wu, F.C., Hu, Z.Y.,
The LLE and a linear mapping,
PR(39), No. 9, September 2006, pp. 1799-1804.
Elsevier DOI 0606
Locally linear embedding (LLE); Linear mapping; Principal component analysis BibRef

Tao, Q.[Qing], Wu, G.W.[Gao-Wei], Wang, J.[Jue],
Learning linear PCA with convex semi-definite programming,
PR(40), No. 10, October 2007, pp. 2633-2640.
Elsevier DOI 0707
Principal component analysis; Statistical learning theory; Support vector machines; Margin; Maximal margin algorithm; Semi-definite programming; Robustness BibRef

Tzimiropoulos, G., Mitianoudis, N., Stathaki, T.,
Robust Recognition of Planar Shapes Under Affine Transforms Using Principal Component Analysis,
SPLetters(14), No. 10, October 2007, pp. 723-726.
IEEE DOI 0711
BibRef

Kumar, K.V.[Kadappagari Vijaya], Negi, A.[Atul],
SubXPCA and a generalized feature partitioning approach to principal component analysis,
PR(41), No. 4, April 2008, pp. 1398-1409.
Elsevier DOI 0801
Dimensionality reduction; Principal component analysis; Sub-pattern based PCA; Feature partitioning BibRef

Kumar, K.V.[Kadappagari Vijaya], Negi, A.[Atul],
Novel approaches to principal component analysis of image data based on feature partitioning framework,
PRL(29), No. 3, 1 February 2008, pp. 254-264.
Elsevier DOI 0801
Dimensionality reduction; PCA; Image principal component analysis; Feature partitioning; Face recognition BibRef

Dambreville, S.[Samuel], Rathi, Y.[Yogesh], Tannenbaum, A.[Allen],
A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors,
PAMI(30), No. 8, August 2008, pp. 1385-1399.
IEEE DOI 0806
BibRef
Earlier:
Shape-Based Approach to Robust Image Segmentation using Kernel PCA,
CVPR06(I: 977-984).
IEEE DOI 0606
BibRef
And:
A Shape-Based Approach to Robust Image Segmentation,
ICIAR06(I: 173-183).
Springer DOI 0610
BibRef
And: A2, A1, A3:
Comparative Analysis of Kernel Methods for Statistical Shape Learning,
CVAMIA06(96-107).
Springer DOI 0605
BibRef

Majumdar, A.,
Image compression by sparse PCA coding in curvelet domain,
SIViP(3), No. 1, January 2009, pp. xx-yy.
Springer DOI 0902
BibRef

Majumdar, A.[Angshul], Ward, R.K.[Rabab K.],
Improved Group Sparse Classifier,
PRL(31), No. 13, 1 October 2010, pp. 1959-1964.
Elsevier DOI 1003
Classification; Quasi-convex optimization; Face recognition; Character recognition BibRef

Urdiales García, C.[Cristina], Dominguez, M., de Trazegnies, C., Sandoval Hernández, F.[Francisco],
A new pyramid-based color image representation for visual localization,
IVC(28), No. 1, Januray 2010, pp. 78-91.
Elsevier DOI 1001
Localization; Color histogram; Principal components; Hierarchical segmentation; Spatial graph BibRef

Wang, H.X.[Hai-Xian],
Structural two-dimensional principal component analysis for image recognition,
MVA(22), No. 2, March 2011, pp. 433-438.
WWW Link. 1103
BibRef
Earlier:
A new feature extraction method for image recognition using structural two-dimensional locality preserving projections,
ICIP09(2037-2040).
IEEE DOI 0911
BibRef

Wang, H.X.[Hai-Xian],
Block principal component analysis with L1-norm for image analysis,
PRL(33), No. 5, 1 April 2012, pp. 537-542.
Elsevier DOI 1202
PCA; 2DPCA; BPCA; L1-norm; Outlier BibRef

Barshan, E.[Elnaz], Ghodsi, A.[Ali], Azimifar, Z.[Zohreh], Jahromi, M.Z.[Mansoor Zolghadri],
Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds,
PR(44), No. 7, July 2011, pp. 1357-1371.
Elsevier DOI 1103
Dimensionality reduction; Principal component analysis (PCA); Kernel methods; Supervised learning; Visualization; Classification; Regression BibRef

He, R.[Ran], Hu, B.G.[Bao-Gang], Zheng, W.S.[Wei-Shi], Kong, X.W.,
Robust Principal Component Analysis Based on Maximum Correntropy Criterion,
IP(20), No. 6, June 2011, pp. 1485-1494.
IEEE DOI 1106
BibRef

He, R.[Ran], Zheng, W.S.[Wei-Shi], Hu, B.G.[Bao-Gang],
Maximum Correntropy Criterion for Robust Face Recognition,
PAMI(33), No. 8, August 2011, pp. 1561-1576.
IEEE DOI 1107
compute sparse representation of face images for recogntion. correntropy more insensitive to outliers. BibRef

Shao, J.[Jian], Wu, F.[Fei], Ouyang, C.[Chuanfei], Zhang, X.[Xiao],
Sparse spectral hashing,
PRL(33), No. 3, 1 February 2012, pp. 271-277.
Elsevier DOI 1201
Semantic hashing; Sparse principal component analysis; Laplacian eigenmap; AdaBoost BibRef

Li, J.[Jun], Tao, D.C.[Da-Cheng], Li, X.L.[Xue-Long],
A probabilistic model for image representation via multiple patterns,
PR(45), No. 11, November 2012, pp. 4044-4053.
Elsevier DOI 1206
Principal component analysis; Probabilistic model BibRef

Bao, B.K., Liu, G., Xu, C., Yan, S.C.,
Inductive Robust Principal Component Analysis,
IP(21), No. 8, August 2012, pp. 3794-3800.
IEEE DOI 1208
BibRef

Bao, B.K., Zhu, G., Shen, J., Yan, S.C.[Shui-Cheng],
Robust Image Analysis With Sparse Representation on Quantized Visual Features,
IP(22), No. 3, March 2013, pp. 860-871.
IEEE DOI 1302
BibRef

Zhang, Z.[Zhao], Zhang, Y.[Yan], Li, F.Z.[Fan-Zhang], Zhao, M.B.[Ming-Bo], Zhang, L.[Li], Yan, S.C.[Shui-Cheng],
Discriminative sparse flexible manifold embedding with novel graph for robust visual representation and label propagation,
PR(61), No. 1, 2017, pp. 492-510.
Elsevier DOI 1705
Flexible manifold embedding BibRef

Liwicki, S.[Stephan], Tzimiropoulos, G.[Georgios], Zafeiriou, S.P.[Stefanos P.], Pantic, M.[Maja],
Euler Principal Component Analysis,
IJCV(101), No. 3, February 2013, pp. 498-518.
WWW Link. 1303
robust dissimilarity based on Euler representation of complex numbers. BibRef

Panagakis, Y.[Yannis], Nicolaou, M.A., Zafeiriou, S.P.[Stefanos P.], Pantic, M.,
Robust Correlated and Individual Component Analysis,
PAMI(38), No. 8, August 2016, pp. 1665-1678.
IEEE DOI 1608
correlation methods BibRef

Papamakarios, G.[Georgios], Panagakis, Y.[Yannis], Zafeiriou, S.P.[Stefanos P.],
Generalised Scalable Robust Principal Component Analysis,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Bahri, M., Panagakis, Y.[Yannis], Zafeiriou, S.P.[Stefanos P.],
Robust Kronecker-Decomposable Component Analysis for Low-Rank Modeling,
ICCV17(3372-3381)
IEEE DOI 1802
image denoising, learning (artificial intelligence), Tensile stress BibRef

Xue, N., Panagakis, Y.[Yannis], Zafeiriou, S.P.[Stefanos P.],
Side Information in Robust Principal Component Analysis: Algorithms and Applications,
ICCV17(4327-4335)
IEEE DOI 1802
face recognition, image denoising, image motion analysis, learning (artificial intelligence), Sparse matrices BibRef

Kallas, M.[Maya], Honeine, P.[Paul], Richard, C.[Cédric], Francis, C.[Clovis], Amoud, H.[Hassan],
Non-negativity constraints on the pre-image for pattern recognition with kernel machines,
PR(46), No. 11, November 2013, pp. 3066-3080.
Elsevier DOI 1306
Kernel machines; Machine learning; SVM; Kernel PCA; Pre-image problem; Non-negativity constraints; Nonlinear denoising; Pattern recognition BibRef

Qian, J.J.[Jian-Jun], Yang, J.[Jian], Gao, G.[Guangwei],
Discriminative histograms of local dominant orientation (D-HLDO) for biometric image feature extraction,
PR(46), No. 10, October 2013, pp. 2724-2739.
Elsevier DOI 1306
Feature extraction; Principal component analysis (PCA); Biometrics; Image representation; Linear discriminant analysis (LDA) BibRef

Candès, E.J.[Emmanuel J.], Li, X.D.[Xiao-Dong], Ma, Y.[Yi], Wright, J.[John],
Robust principal component analysis?,
JACM(58), No. 3, 2011, 11. 1402
BibRef

Zeng, X.Q.A.[Xue-Qi-Ang], Li, G.Z.[Guo-Zheng],
Incremental partial least squares analysis of big streaming data,
PR(47), No. 11, 2014, pp. 3726-3735.
Elsevier DOI 1407
Feature extraction BibRef

Han, F.[Fang], Liu, H.[Han],
High Dimensional Semiparametric Scale-Invariant Principal Component Analysis,
PAMI(36), No. 10, October 2014, pp. 2016-2032.
IEEE DOI 1410
Gaussian distribution BibRef

Hasanbelliu, E.[Erion], Giraldo, L.S.[Luis Sanchez], Principe, J.C.[Jose C.],
Information Theoretic Shape Matching,
PAMI(36), No. 12, December 2014, pp. 2436-2451.
IEEE DOI 1411
BibRef
Earlier:
A Recursive Online Kernel PCA Algorithm,
ICPR10(169-172).
IEEE DOI 1008
Accuracy BibRef

Vonesch, C.[Cédric], Stauber, F.[Frédéric], Unser, M.[Michael],
Steerable PCA for Rotation-Invariant Image Recognition,
SIIMS(8), No. 3, 2015, pp. 1857-1873.
DOI Link 1511
BibRef

Hauberg, S.[Soren], Feragen, A.[Aasa], Enficiaud, R., Black, M.J.[Michael J.],
Scalable Robust Principal Component Analysis Using Grassmann Averages,
PAMI(38), No. 11, November 2016, pp. 2298-2311.
IEEE DOI 1610
BibRef
Earlier: A1, A2, A4, Only:
Grassmann Averages for Scalable Robust PCA,
CVPR14(3810-3817)
IEEE DOI 1409
Approximation methods. Grassmann manifold BibRef

Wang, Z.H.[Zhen-Hua], Fan, B.[Bin], Wang, G., Wu, F.C.[Fu-Chao],
Exploring Local and Overall Ordinal Information for Robust Feature Description,
PAMI(38), No. 11, November 2016, pp. 2198-2211.
IEEE DOI 1610
BibRef
Earlier: A1, A2, A4, Only:
Affine Subspace Representation for Feature Description,
ECCV14(VII: 94-108).
Springer DOI 1408
Distortion. Affine distortions due to viewpoint changes. PCA on patches. BibRef

Itoh, H.[Hayato], Imiya, A.[Atsushi], Sakai, T.[Tomoya],
Pattern recognition in multilinear space and its applications: mathematics, computational algorithms and numerical validations,
MVA(27), No. 8, November 2016, pp. 1259-1273.
Springer DOI 1612
BibRef
Earlier:
Low-Dimensional Tensor Principle Component Analysis,
CAIP15(I:715-726).
Springer DOI 1511

See also Dimension Reduction and Construction of Feature Space for Image Pattern Recognition. BibRef

Landa, B.[Boris], Shkolnisky, Y.[Yoel],
Steerable Principal Components for Space-Frequency Localized Images,
SIIMS(10), No. 2, 2017, pp. 508-534.
DOI Link 1708
BibRef

Puggini, L.[Luca], McLoone, S.[Seán],
Forward Selection Component Analysis: Algorithms and Applications,
PAMI(39), No. 12, December 2017, pp. 2395-2408.
IEEE DOI 1711
Input variables, Matching pursuit algorithms, Power capacitors, Principal component analysis, Signal processing algorithms, Unsupervised dimensionality reduction, feature selection, subset, selection BibRef

Wang, Q., Gao, Q., Gao, X., Nie, F.,
L_2,p-Norm Based PCA for Image Recognition,
IP(27), No. 3, March 2018, pp. 1336-1346.
IEEE DOI 1801
Covariance matrices, Feature extraction, Image reconstruction, Linear programming, Measurement, Principal component analysis, dimensionality reduction BibRef

Fan, C.X.[Chun-Xiao], Hong, X.P.[Xiao-Peng], Tian, L.[Lei], Ming, Y.[Yue], Pietikäinen, M.[Matti], Zhao, G.Y.[Guo-Ying],
PCANet-II: When PCANet Meets the Second Order Pooling,
IEICE(E101-D), No. 8, August 2018, pp. 2159-2162.
WWW Link. 1808
BibRef

Lu, Y., Lai, Z., Li, X., Wong, W.K., Yuan, C., Zhang, D.,
Low-Rank 2-D Neighborhood Preserving Projection for Enhanced Robust Image Representation,
Cyber(49), No. 5, May 2019, pp. 1859-1872.
IEEE DOI 1903
Robustness, Principal component analysis, Sparse matrices, Image representation, Convergence, Noise measurement, Optimization, robust feature extraction BibRef

Nie, F., Zhang, H., Zhang, R., Li, X.,
Robust Multiple Rank-k Bilinear Projections for Unsupervised Learning,
IP(28), No. 5, May 2019, pp. 2574-2583.
IEEE DOI 1903
Feature extraction, Linear programming, Principal component analysis, Image reconstruction, nearest reconstruction BibRef

Lai, Z., Mo, D., Wen, J., Shen, L., Wong, W.K.,
Generalized Robust Regression for Jointly Sparse Subspace Learning,
CirSysVideo(29), No. 3, March 2019, pp. 756-772.
IEEE DOI 1903
Robustness, Feature extraction, Radio frequency, Face recognition, Dimensionality reduction, Principal component analysis, small-class problem BibRef

Wang, W.Q.[Wen-Qi], Aggarwal, V.[Vaneet], Aeron, S.C.[Shu-Chin],
Principal component analysis with tensor train subspace,
PRL(122), 2019, pp. 86-91.
Elsevier DOI 1904
BibRef

Zhou, N., Cheng, H., Qin, J., Du, Y., Chen, B.,
Robust High-Order Manifold Constrained Sparse Principal Component Analysis for Image Representation,
CirSysVideo(29), No. 7, July 2019, pp. 1946-1961.
IEEE DOI 1907
Principal component analysis, Robustness, Manifolds, Kernel, Image reconstruction, Task analysis, Correntropy, high-order manifold BibRef

Sun, Y.Q.[Yao-Qi], Li, L.[Liang], Zheng, L.[Liang], Hu, J.[Ji], Li, W.C.[Wen-Chao], Jiang, Y.T.[Ya-Tong], Yan, C.G.[Cheng-Gang],
Image Classification Base on PCA of Multi-View Deep Representation,
JVCIR(62), 2019, pp. 253-258.
Elsevier DOI 1908
Depth, RGB, use PCA. Image classification, Principal component analysis, Multi-view depth characters BibRef

Vargas, H., Arguello, H.,
A Low-Rank Model for Compressive Spectral Image Classification,
GeoRS(57), No. 12, December 2019, pp. 9888-9899.
IEEE DOI 1912
Image coding, Hyperspectral imaging, Feature extraction, Image reconstruction, Principal component analysis, total variation (TV) BibRef

Gelvez, T., Arguello, H.,
Nonlocal Low-Rank Abundance Prior for Compressive Spectral Image Fusion,
GeoRS(59), No. 1, January 2021, pp. 415-425.
IEEE DOI 2012
Image coding, Inverse problems, Image fusion, Mathematical model, Silicon, Spatial resolution, nonlocal self-similarities BibRef

Dadon, A.[Alon], Mandelmilch, M.[Moshe], Ben-Dor, E.[Eyal], Sheffer, E.[Efrat],
Sequential PCA-based Classification of Mediterranean Forest Plants using Airborne Hyperspectral Remote Sensing,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Ren, Z., Sun, Q., Wu, B., Zhang, X., Yan, W.,
Learning Latent Low-Rank and Sparse Embedding for Robust Image Feature Extraction,
IP(29), 2020, pp. 2094-2107.
IEEE DOI 2001
Feature extraction, Principal component analysis, Sparse matrices, Learning systems, Dictionaries, Task analysis, face recognition BibRef

Xiao, W., Huang, X., He, F., Silva, J., Emrani, S., Chaudhuri, A.,
Online Robust Principal Component Analysis With Change Point Detection,
MultMed(22), No. 1, January 2020, pp. 59-68.
IEEE DOI 2001
Principal component analysis, Sparse matrices, Matrix decomposition, Surveillance, Big Data, principal component analysis BibRef

Chen, X.H.[Xiu-Hong], Sun, H.Q.[Hui-Qiang],
Joint low-rank project embedding and optimal mean principal component analysis,
IET-IPR(14), No. 8, 19 June 2020, pp. 1457-1466.
DOI Link 2005
BibRef

Machidon, A.L.[Alina L.], del Frate, F.[Fabio], Picchiani, M.[Matteo], Machidon, O.M.[Octavian M.], Ogrutan, P.L.[Petre L.],
Geometrical Approximated Principal Component Analysis for Hyperspectral Image Analysis,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Park, C.H.[Cheong Hee], Lee, G.H.[Gyeong-Hoon],
Comparison of incremental linear dimension reduction methods for streaming data,
PRL(135), 2020, pp. 15-21.
Elsevier DOI 2006
Concept drift, Incremental dimension reduction method, Linear discriminant analysis, Principal component analysis, Streaming data BibRef

Zarmehi, N., Amini, A., Marvasti, F.,
Low Rank and Sparse Decomposition for Image and Video Applications,
CirSysVideo(30), No. 7, July 2020, pp. 2046-2056.
IEEE DOI 2007
Approximation algorithms, Sparse matrices, Optimization, Principal component analysis, Video surveillance, Convergence, video surveillance BibRef

Bueso, D., Piles, M., Camps-Valls, G.,
Nonlinear PCA for Spatio-Temporal Analysis of Earth Observation Data,
GeoRS(58), No. 8, August 2020, pp. 5752-5763.
IEEE DOI 2007
Principal component analysis, Feature extraction, Earth, Kernel, Meteorology, Data mining, Dimensionality reduction, spatio-temporal data BibRef

Tran, T.P.T.[Thi Phuong Thao], Douzal-Chouakria, A.[Ahlame], Yazdi, S.V.[Saeed Varasteh], Honeine, P.[Paul], Gallinari, P.[Patrick],
Interpretable time series kernel analytics by pre-image estimation,
AI(286), 2020, pp. 103342.
Elsevier DOI 2008
Pre-image problem, Time series, Kernel machinery, Time series averaging, Kernel PCA, Dictionary learning, Representation learning BibRef

Yang, F.[Feng], Ma, Z.[Zheng], Xie, M.[Mei],
Visual Recognition Method Based on Hybrid KPCA Network,
IEICE(E103-D), No. 9, September 2020, pp. 2015-2018.
WWW Link. 2009
BibRef

Wu, D., Zhang, H., Nie, F., Wang, R., Yang, C., Jia, X., Li, X.,
Double-Attentive Principle Component Analysis,
SPLetters(27), 2020, pp. 1814-1818.
IEEE DOI 2011
Principal component analysis, Analytical models, Image reconstruction, Signal processing algorithms, Convergence, image reconstruction BibRef

Cai, H., Hamm, K., Huang, L., Li, J., Wang, T.,
Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank Estimation,
SPLetters(28), 2021, pp. 116-120.
IEEE DOI 2101
Sparse matrices, Principal component analysis, Signal processing algorithms, Matrix decomposition, Tools, outlier removal BibRef

Dong, Y.N.[Yan-Ni], Liang, T.Y.[Tian-Yang], Zhang, Y.X.[Yu-Xiang], Du, B.[Bo],
Spectral-Spatial Weighted Kernel Manifold Embedded Distribution Alignment for Remote Sensing Image Classification,
Cyber(51), No. 6, June 2021, pp. 3185-3197.
IEEE DOI 2106
Remote sensing, Kernel, Manifolds, Distortion, Support vector machines, Principal component analysis, weighted kernel BibRef

Zhang, F.[Feng], Wang, J.J.[Jian-Jun], Wang, W.D.[Wen-Dong], Xu, C.[Chen],
Low-Tubal-Rank Plus Sparse Tensor Recovery With Prior Subspace Information,
PAMI(43), No. 10, October 2021, pp. 3492-3507.
IEEE DOI 2109
Tensile stress, Robustness, Principal component analysis, Convex functions, Face, Data models, Singular value decomposition, ADMM BibRef

Kong, W.C.[Wei-Chao], Zhang, F.[Feng], Qin, W.J.[Wen-Jin], Wang, J.J.[Jian-Jun],
Low-Tubal-Rank tensor recovery with multilayer subspace prior learning,
PR(140), 2023, pp. 109545.
Elsevier DOI 2305
Tensor robust principal component analysis, Tensor completion, Multilayer subspace prior information, ADMM, T-SVD BibRef

Hou, J.Y.[Jing-Yao], Zhang, F.[Feng], Qiu, H.Q.[Hai-Quan], Wang, J.J.[Jian-Jun], Wang, Y.[Yao], Meng, D.Y.[De-Yu],
Robust Low-Tubal-Rank Tensor Recovery From Binary Measurements,
PAMI(44), No. 8, August 2022, pp. 4355-4373.
IEEE DOI 2207
Tensors, Synthetic aperture radar, Quantization (signal), Image reconstruction, Matrix decomposition, Electron tubes, adaptivity BibRef

Nie, F.P.[Fei-Ping], Wu, D.Y.[Dan-Yang], Wang, R.[Rong], Li, X.L.[Xue-Long],
Truncated Robust Principle Component Analysis With A General Optimization Framework,
PAMI(44), No. 2, February 2022, pp. 1081-1097.
IEEE DOI 2201
Robustness, Principal component analysis, Analytical models, Optimization, Image reconstruction, Data models, Adaptive optics, non-convex optimization BibRef

Qiu, Y.N.[Yu-Ning], Zhou, G.X.[Guo-Xu], Huang, Z.H.[Zhen-Hao], Zhao, Q.B.[Qi-Bin], Xie, S.L.[Sheng-Li],
Efficient Tensor Robust PCA Under Hybrid Model of Tucker and Tensor Train,
SPLetters(29), 2022, pp. 627-631.
IEEE DOI 2203
Tensors, Matrix decomposition, Computational modeling, Optimization, Minimization, Matrix converters, tensor robust principal component analysis BibRef

Gao, Y.L.[Yun-Long], Lin, T.T.[Ting-Ting], Pan, J.Y.[Jin-Yan], Nie, F.P.[Fei-Ping], Xie, Y.W.[You-Wei],
Fuzzy Sparse Deviation Regularized Robust Principal Component Analysis,
IP(31), 2022, pp. 5645-5660.
IEEE DOI 2209
Principal component analysis, Image reconstruction, Robustness, Data models, Analytical models, Measurement uncertainty, sparse BibRef

Liu, T.H.[Tian-Hao], Díaz-Pachón, D.A.[Daniel Andrés], Rao, J. .S.I.[J. Sun-Il], Dazard, J.E.[Jean-Eudes],
High Dimensional Mode Hunting Using Pettiest Components Analysis,
PAMI(45), No. 4, April 2023, pp. 4637-4649.
IEEE DOI 2303
Principal component analysis, Eigenvalues and eigenfunctions, Tumors, Time complexity, Partitioning algorithms, Kernel, principal components analysis BibRef

Virta, J.[Joni], Artemiou, A.[Andreas],
Poisson PCA for matrix count data,
PR(138), 2023, pp. 109401.
Elsevier DOI 2303
Discrete data, Kronecker model, Matrix normal distribution, Poisson log-normal distribution BibRef

Ali, T.M.F.[T. M. Feroz], Chaudhuri, S.[Subhasis],
Theoretical Analysis of Null Foley-Sammon Transform and its Implications,
PAMI(45), No. 5, May 2023, pp. 6445-6459.
IEEE DOI 2304
Transforms, Measurement, Training, Principal component analysis, Anomaly detection, Feature extraction, small sample size data BibRef

Gao, K.X.[Kai-Xin], Huang, Z.H.[Zheng-Hai],
Tensor Robust Principal Component Analysis via Tensor Fibered Rank and l_p Minimization,
SIIMS(16), No. 1, 2023, pp. 423-460.
DOI Link 2305
BibRef

Panhuber, R.[Reinhard],
Fast, Efficient, and Viable Compressed Sensing, Low-Rank, and Robust Principle Component Analysis Algorithms for Radar Signal Processing,
RS(15), No. 8, 2023, pp. 2216.
DOI Link 2305
BibRef

Kang, Z.[Zhao], Liu, H.F.[Hong-Fei], Li, J.X.[Jiang-Xin], Zhu, X.F.[Xiao-Feng], Tian, L.[Ling],
Self-paced principal component analysis,
PR(142), 2023, pp. 109692.
Elsevier DOI 2307
Dimension reduction, Outliers, Manifold learning BibRef

Zhang, H.Y.[Hong-Yuan], Zhu, Y.[Yanan], Li, X.L.[Xue-Long],
Toward Projected Clustering With Aggregated Mapping,
IP(32), 2023, pp. 4103-4113.
IEEE DOI 2307
Feature extraction, Principal component analysis, Manifold learning, Graph neural networks, Data models, unsupervised dimensionality reduction BibRef

Han, Z.[Zhi], Zhang, S.J.[Shao-Jie], Liu, Z.[Zhiyu], Wang, Y.[Yanmei], Yao, J.P.[Jun-Ping], Wang, Y.[Yao],
Tensor Robust Principal Component Analysis With Side Information: Models and Applications,
CirSysVideo(33), No. 8, August 2023, pp. 3713-3725.
IEEE DOI 2308
Tensors, Matrix decomposition, Data models, Noise measurement, Computational modeling, Robots, Analytical models, Low rank, side information BibRef

Bi, P.F.[Peng-Fei], Du, X.[Xue],
Arbitrary Triangle Structure Adaptive Mean PCA and Image Recognition,
CirSysVideo(34), No. 2, February 2024, pp. 754-769.
IEEE DOI 2402
Principal component analysis, Robustness, Measurement, Image reconstruction, Feature extraction, Optimization, image recognition BibRef

Liu, Y.[Youfa], Du, B.[Bo], Chen, Y.Y.[Yong-Yong], Zhang, L.[Lefei], Gong, M.M.[Ming-Ming], Tao, D.C.[Da-Cheng],
Convex-Concave Tensor Robust Principal Component Analysis,
IJCV(132), No. 5, May 2024, pp. 1721-1747.
Springer DOI 2405
BibRef

Peng, J.J.[Jiang-Jun], Wang, H.L.[Hai-Lin], Cao, X.Y.[Xiang-Yong], Jia, X.X.[Xi-Xi], Zhang, H.Y.[Hong-Ying], Meng, D.Y.[De-Yu],
Stable Local-Smooth Principal Component Pursuit,
SIIMS(17), No. 2, 2024, pp. 1182-1205.
DOI Link Code:
WWW Link. 2407
BibRef

Li, T.[Tao], Ma, J.W.[Jin-Wen],
T-SVD Based Non-convex Tensor Completion and Robust Principal Component Analysis,
ICPR21(6980-6987)
IEEE DOI 2105
Tensors, Machine learning, Minimization, Particle measurements, Task analysis, Optimization BibRef

Fan, Y.F.[Yi-Fei], Dahiya, N.[Navdeep], Bignardi, S.[Samuel], Sandhu, R.[Romeil], Yezzi, A.J.[Anthony J.],
Directionally Paired Principal Component Analysis for Bivariate Estimation Problems,
ICPR21(10180-10187)
IEEE DOI 2105
BibRef
Earlier: A2, A1, A3, A4, A5:
Dependently Coupled Principal Component Analysis for Bivariate Inversion Problems,
ICPR21(10592-10599)
IEEE DOI 2105
Analytical models, Correlation, Computational modeling, Estimation, Data models, Pattern recognition, Image reconstruction. Training, Manifolds, Image processing, Approximation error, Minimization, Shape Analysis BibRef

Ghojogh, B.[Benyamin], Karray, F.[Fakhri], Crowley, M.[Mark],
Image Structure Subspace Learning Using Structural Similarity Index,
ICIAR19(I:33-44).
Springer DOI 1909
BibRef
And:
Principal Component Analysis Using Structural Similarity Index for Images,
ICIAR19(I:77-88).
Springer DOI 1909
BibRef
And:
Locally Linear Image Structural Embedding for Image Structure Manifold Learning,
ICIAR19(I:126-138).
Springer DOI 1909
BibRef

El Fattahi, L., Sbai, E.H.,
Kernel entropy principal component analysis using Parzen estimator,
ISCV18(1-8)
IEEE DOI 1807
feature extraction, maximum entropy methods, pattern clustering, principal component analysis, Parzen estimator, Shannon entropy, kernel entropy principal component analysis (KEPCA) BibRef

Wang, M.J.[Meng-Jiao], Panagakis, Y.[Yannis], Snape, P.[Patrick], Zafeiriou, S.P.[Stefanos P.],
Learning the Multilinear Structure of Visual Data,
CVPR17(6053-6061)
IEEE DOI 1711
Databases, Matrix decomposition, Principal component analysis, Shape, Tensile stress, Visualization BibRef

Zhou, P., Feng, J.,
Outlier-Robust Tensor PCA,
CVPR17(3938-3946)
IEEE DOI 1711
Algorithm design and analysis, Discrete Fourier transforms, Optimization, Principal component analysis, Sparse matrices, Tensile, stress BibRef

Itoh, H.[Hayato], Imiya, A.[Atsushi], Sakai, T.[Tomoya],
Analysis of Multilinear Subspaces Based on Geodesic Distance,
CAIP17(I: 384-396).
Springer DOI 1708
BibRef

Wang, Q., Gao, Q.,
Robust 2DPCA and Its Application,
Robust16(1152-1158)
IEEE DOI 1612
BibRef

Pena, R., Bresson, X., Vandergheynst, P.[Pierre],
Source localization on graphs via L_1 recovery and spectral graph theory,
IVMSP16(1-5)
IEEE DOI 1608
Diffusion processes BibRef

Shahid, N., Kalofolias, V., Bresson, X., Bronstein, M.M., Vandergheynst, P.,
Robust Principal Component Analysis on Graphs,
ICCV15(2812-2820)
IEEE DOI 1602
Benchmark testing BibRef

Zhang, C.[Chao], Heeren, B.[Behrend], Rumpf, M.[Martin], Smith, W.A.P.[William A. P.],
Shell PCA: Statistical Shape Modelling in Shell Space,
ICCV15(1671-1679)
IEEE DOI 1602
Computational modeling. Surfaces with non-zero thickness. Deformation, articulation. BibRef

Inoue, K.[Kohei], Hara, K.[Kenji], Urahama, K.[Kiichi],
A Unified View of Two-Dimensional Principal Component Analyses,
SSSPR12(566-574).
Springer DOI 1211
BibRef

Mignon, A.[Alexis], Jurie, F.[Frederic],
PCCA: A new approach for distance learning from sparse pairwise constraints,
CVPR12(2666-2672).
IEEE DOI 1208
BibRef

Ayazoglu, M.[Mustafa], Sznaier, M.[Mario], Camps, O.I.[Octavia I.],
Fast algorithms for structured robust principal component analysis,
CVPR12(1704-1711).
IEEE DOI 1208
BibRef

Lim, K.L.[Kart-Leong], Galoogahi, H.K.,
Shape Classification Using Local and Global Features,
PSIVT10(115-120).
IEEE DOI 1011
Histogram of Oriented Gradient (HOG). PCA for both local and global. BibRef

Duanduan, Y.[Yang], Sluzek, A.[Andrzej],
Performance evaluation of low-dimensional sifts,
ICIP10(2729-2732).
IEEE DOI 1009
simplifications of SIFT, analysis. PCA-SIFT. BibRef

Hui, K.H.[Kang-Hua], Wang, C.H.[Chun-Heng], Xiao, B.H.[Bai-Hua],
Globally-Preserving Based Locally Linear Embedding,
ICPR10(531-534).
IEEE DOI 1008
BibRef

Bruneau, P.[Pierrick], Gelgon, M.[Marc], Picarougne, F.[Fabien],
Aggregation of Probabilistic PCA Mixtures with a Variational-Bayes Technique Over Parameters,
ICPR10(702-705).
IEEE DOI 1008
BibRef

Negi, A.[Atul], Kadappa, V.K.[Vijaya Kumar],
SubXPCA versus PCA: A Theoretical Investigation,
ICPR10(4170-4173).
IEEE DOI 1008
BibRef

Sommer, S.[Stefan], Lauze, F.[François], Hauberg, S.[Søren], Nielsen, M.[Mads],
Manifold Valued Statistics, Exact Principal Geodesic Analysis and the Effect of Linear Approximations,
ECCV10(VI: 43-56).
Springer DOI 1009
Effect of linearization when using Principal Geodesic Analysis. BibRef

Mu, Y.D.[Ya-Dong], Sun, J.[Ju], Han, T.X.[Tony X.], Cheong, L.F.[Loong-Fah], Yan, S.C.[Shui-Cheng],
Randomized Locality Sensitive Vocabularies for Bag-of-Features Model,
ECCV10(III: 748-761).
Springer DOI 1009
BibRef

Mu, Y.D.[Ya-Dong], Shen, J.L.[Jia-Lie], Yan, S.C.[Shui-Cheng],
Weakly-supervised hashing in kernel space,
CVPR10(3344-3351).
IEEE DOI 1006
BibRef

Ozay, N.[Necmiye], Sznaier, M.[Mario], Lagoa, C.[Constantino], Camps, O.I.[Octavia I.],
GPCA with denoising: A moments-based convex approach,
CVPR10(3209-3216).
IEEE DOI 1006
Generalized Principal Component Analysis BibRef

Inoue, K.[Kohei], Hara, K.[Kenji], Urahama, K.[Kiichi],
Robust multilinear principal component analysis,
ICCV09(591-597).
IEEE DOI 0909
BibRef

Serra, J.[Jean],
Adaptive lattices on the unit sphere. Application to remote sensing,
ICIP09(2257-2260).
IEEE DOI 0911
Study color, luminance and hue distributions. BibRef

Laparra, V.[Valero], Camps-Valls, G.[Gustavo], Malo, J.[Jesus],
PCA Gaussianization for image processing,
ICIP09(3985-3988).
IEEE DOI 0911

See also Recovering wavelet relations using SVM for image denoising. BibRef

Ogawa, T.[Takahiro], Haseyama, M.[Miki],
Kernel PCA-based semantic feature estimation approach for similar image retrieval,
ICIP08(965-968).
IEEE DOI 0810
BibRef

Mei, L.[Lin], Figl, M.[Michael], Darzi, A.[Ara], Rueckert, D.[Daniel], Edwards, P.[Philip],
Sample Sufficiency and PCA Dimension for Statistical Shape Models,
ECCV08(IV: 492-503).
Springer DOI 0810
BibRef

Zhao, D.L.[De-Li], Lin, Z.C.[Zhou-Chen], Tang, X.[Xiaoou],
Laplacian PCA and Its Applications,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Maret, Y., Nikolopoulos, S., Dufaux, F., Ebrahimi, T., Nikolaidis, N.,
A Novel Replica Detection System using Binary Classifiers, R-Trees, and PCA,
ICIP06(925-928).
IEEE DOI 0610
BibRef

Sarkis, M., Dawy, Z., Obermeier, F., Diepold, K.,
Automatic Model-Order Selection for PCA,
ICIP06(933-936).
IEEE DOI 0610
BibRef

Jin, Z.[Zhong], Davoine, F.[Franck], Lou, Z.[Zhen], Yang, J.Y.[Jing-Yu],
A Novel PCA-Based Bayes Classifier and Face Analysis,
ICB06(144-150).
Springer DOI 0601
BibRef

Hosic, S.[Sabina], Hocanin, A.[Aykut], Demirel, H.[Hasan],
Unequal Error Protection Using Convolutional Codes for PCA-Coded Images,
ICIAR05(335-342).
Springer DOI 0509
BibRef

Jin, Z.[Zhong], Davoine, F.[Franck],
Orthogonal ICA representation of images,
ICARCV04(I: 369-374).
IEEE DOI 0412
BibRef

Tanaka, T.,
Generalized subspace rules for on-line PCA and their application in signal and image compression,
ICIP04(III: 1895-1898).
IEEE DOI 0505
BibRef

Romaniuk, B., Guilloux, V., Desvignes, M., Deshayes, M.J.,
Partially observed objects localization with PCA and KPCA models,
Southwest04(80-84).
IEEE DOI 0411
BibRef

Ke, Y.[Yan], Sukthankar, R.,
PCA-SIFT: a more distinctive representation for local image descriptors,
CVPR04(II: 506-513).
IEEE DOI 0408

See also Distinctive Image Features from Scale-Invariant Keypoints. BibRef

Meltzer, J.[Jason], Yang, M.H.[Ming-Hsuan], Gupta, R.[Rakesh], Soatto, S.[Stefano],
Multiple View Feature Descriptors from Image Sequences via Kernel Principal Component Analysis,
ECCV04(Vol I: 215-227).
Springer DOI 0405
BibRef

Le Bihan, N., Sangwine, S.J.,
Quaternion principal component analysis of color images,
ICIP03(I: 809-812).
IEEE DOI 0312
BibRef

Zeng, X.Y.[Xiang-Yan], Chen, Y.W.[Yen-Wei], Nakao, Z.,
Image feature representation by the subspace of nonlinear PCA,
ICPR02(II: 228-231).
IEEE DOI 0211
BibRef

Hegazy, D.[Doaa], Denzler, J.[Joachim],
Combining Appearance and Range Based Information for Multi-class Generic Object Recognition,
CIARP09(741-748).
Springer DOI 0911
BibRef
Earlier:
Generic Object Recognition Using Boosted Combined Features,
RobVis08(355-366).
Springer DOI 0802
BibRef

Drexler, C., Mattern, F., Denzler, J.,
Appearance Based Generic Object Modeling and Recognition Using Probabilistic Principal Component Analysis,
DAGM02(100 ff.).
Springer DOI 0303
BibRef

Perantonis, S.J., Petridis, S., Virvilis, V.,
Supervised Principal Component Analysis Using a Smooth Classifier Paradigm,
ICPR00(Vol II: 109-112).
IEEE DOI 0009
BibRef

Surendro, K.[Kridanto], Anzai, Y.[Yuichiro],
Non-rigid object recognition using principal component analysis and geometric hashing,
CAIP97(50-57).
Springer DOI 9709
BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Invariants -- ICA, Independent Component Analysis .