14.1.4.4 PCA, Principal Component Analysis, Data Dimensionality Reduction

Loog, M.[Marco], Duin, R.P.W., Haeb-Umbach, R.,
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria,
PAMI(23), No. 7, July 2001, pp. 762-766.
IEEE DOI 0108
BibRef
Earlier: A2, A1, A3:
Multi-class Linear Feature Extraction by Nonlinear PCA,
ICPR00(Vol II: 398-401).
IEEE DOI 0009
BibRef

Loog, M.[Marco], Duin, R.P.W.[Robert P.W.],
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion,
PAMI(26), No. 6, June 2004, pp. 732-739.
IEEE Abstract. 0404
BibRef

Loog, M.[Marco], Duin, R.P.W.[Robert P.W.],
The Dipping Phenomenon,
SSSPR12(310-317).
Springer DOI 1211
BibRef

Loog, M.[Marco], van Ginneken, B.[Bram], Duin, R.P.W.[Robert P.W.],
Dimensionality reduction of image features using the canonical contextual correlation projection,
PR(38), No. 12, December 2005, pp. 2409-2418.
Elsevier DOI 0510
BibRef
Earlier:
Dimensionality Reduction by Canonical Contextual Correlation Projections,
ECCV04(Vol I: 562-573).
Springer DOI 0405
BibRef

Loog, M.[Marco],
On an alternative formulation of the Fisher criterion that overcomes the small sample problem,
PR(40), No. 6, June 2007, pp. 1753-1755.
Elsevier DOI 0704
BibRef
Earlier:
Conditional Linear Discriminant Analysis,
ICPR06(II: 387-390).
IEEE DOI 0609
Fisher criterion; Feature extraction; Small sample problem; Counterexample BibRef

Loog, M.[Marco], de Ridder, D.[Dick],
Local Discriminant Analysis,
ICPR06(III: 328-331).
IEEE DOI 0609
BibRef

Qin, A.K., Suganthan, P.N., Loog, M.,
Efficient Feature Extraction Based on Regularized Uncorrelated Chernoff Discriminant Analysis,
ICPR06(III: 125-128).
IEEE DOI 0609
BibRef

Choi, S.J.[Seung-Jin],
Sequential EM learning for subspace analysis,
PRL(25), No. 14, 15 October 2004, pp. 1559-1567.
Elsevier DOI 0410
PCA for sub space analysis. BibRef

Zheng, Z.L.[Zhong-Long], Yang, J.[Jie],
Supervised locality pursuit embedding for pattern classification,
IVC(24), No. 8, August 2006, pp. 819-826.
Elsevier DOI 0608
Dimensionality reduction; Principal component analysis; Linear discriminant analysis; Locality pursuit embedding; Supervised learning methods BibRef

Zubko, V., Kaufman, Y.J., Burg, R.I., Martins, J.V.,
Principal Component Analysis of Remote Sensing of Aerosols Over Oceans,
GeoRS(45), No. 3, March 2007, pp. 730-745.
IEEE DOI 0703
BibRef

Cho, M.K.[Min-Kook], Park, H.Y.[Hye-Young],
A feature analysis for dimension reduction based on a data generation model with class factors and environment factors,
CVIU(113), No. 9, September 2009, pp. 1005-1016.
Elsevier DOI 0907
Pattern classification; Feature analysis; Dimension reduction; PCA (principal component analysis); LDA (linear discriminant analysis); Data generation model; Class factor; Environment factor BibRef

Tasoulis, S.K., Tasoulis, D.K., Plagianakos, V.P.,
Enhancing principal direction divisive clustering,
PR(43), No. 10, October 2010, pp. 3391-3411.
Elsevier DOI 1007
Clustering; Principal component analysis; Kernel density estimation BibRef

Tasoulis, S.K., Tasoulis, D.K., Plagianakos, V.P.,
Random direction divisive clustering,
PRL(34), No. 2, 15 January 2013, pp. 131-139.
Elsevier DOI 1212
Clustering; Principal Component Analysis; Random Projection; Kernel Density Estimation BibRef

Torbick, N., Becker, B.,
Evaluating Principal Components Analysis for Identifying Optimal Bands Using Wetland Hyperspectral Measurements From the Great Lakes, USA,
RS(1), No. 3, September 2009, pp. 408-417.
DOI Link 1203
BibRef

Ding, X., He, L., Carin, L.[Lawrence],
Bayesian Robust Principal Component Analysis,
IP(20), No. 12, December 2011, pp. 3419-3430.
IEEE DOI 1112
BibRef

Yektaii, M.[Mahdi], Bhattacharya, P.[Prabir],
A criterion for measuring the separability of clusters and its applications to principal component analysis,
SIViP(5), No. 1, March 2011, pp. 93-104.
WWW Link. 1103
BibRef

Wang, D.H.[Dong-Hui], Kong, S.[Shu],
Feature selection from high-order tensorial data via sparse decomposition,
PRL(33), No. 13, 1 October 2012, pp. 1695-1702.
Elsevier DOI 1208
Dimensionality reduction; Feature selection; Tensor decomposition; High-order principal component analysis; Sparse principal component analysis BibRef

Honeine, P.[Paul],
Online Kernel Principal Component Analysis: A Reduced-Order Model,
PAMI(34), No. 9, September 2012, pp. 1814-1826.
IEEE DOI 1208
BibRef

Gao, J.B.[Jun-Bin], Shi, Q.F.[Qin-Feng], Caetano, T.S.[Tibério S.],
Dimensionality reduction via compressive sensing,
PRL(33), No. 9, 1 July 2012, pp. 1163-1170.
Elsevier DOI 1202
Dimensionality reduction; Sparse models; PCA; Supervised learning; Un-supervised learning; Compressive sensing BibRef

Tu, S.K.[Shi-Kui], Xu, L.[Lei],
A theoretical investigation of several model selection criteria for dimensionality reduction,
PRL(33), No. 9, 1 July 2012, pp. 1117-1126.
Elsevier DOI 1202
Factor analysis; PCA; Dimensionality reduction; Model selection criteria BibRef

Zhu, X.Z.[Xin-Zhong],
Super-class Discriminant Analysis: A novel solution for heteroscedasticity,
PRL(34), No. 5, 1 April 2013, pp. 545-551.
Elsevier DOI 1303
Heteroscedasticity problem; Super-class; Super-class Discriminant Analysis; Divide and conquer BibRef

Kadappa, V.[Vijayakumar], Negi, A.[Atul],
Computational and space complexity analysis of SubXPCA,
PR(46), No. 8, August 2013, pp. 2169-2174.
Elsevier DOI 1304
Dimensionality reduction; Feature extraction; Principal component analysis; Feature partitioning; Space complexity; Time complexity BibRef

Liang, Z.Z.[Zhi-Zheng], Xia, S.X.[Shi-Xiong], Zhou, Y.[Yong], Zhang, L.[Lei], Li, Y.F.[You-Fu],
Feature extraction based on Lp-norm generalized principal component analysis,
PRL(34), No. 9, July 2013, pp. 1037-1045.
Elsevier DOI 1305
Generalized PCA; Lp-norm; Convex function; Face images; UCI data sets BibRef

Villegas, M.[Mauricio], Paredes, R.[Roberto],
On improving robustness of LDA and SRDA by using tangent vectors,
PRL(34), No. 9, July 2013, pp. 1094-1100.
Elsevier DOI 1305
Subspace learning; Dimensionality reduction; Tangent vectors; LDA; SRDA BibRef

Hari Kumar, R., Vinoth Kumar, B.,
Comprehensive analysis of LPG-PCA algorithms in denoising and deblurring of medical images,
IJIST(23), No. 2, 2013, pp. 157-170.
DOI Link principle component analysis, local pixel grouping, denoising, deblurring, image quality measures 1307
BibRef

Hari Kumar, R., Vinoth Kumar, B., Gowthami, S.,
Performance analysis of LPG PCA algorithm in medical images,
IMVIP12(125-128).
IEEE DOI 1302
BibRef

Ulfarsson, M.O., Solo, V.,
Selecting the Number of Principal Components with SURE,
SPLetters(22), No. 2, February 2015, pp. 239-243.
IEEE DOI 1410
Eigenvalues and eigenfunctions BibRef

Lu, M.[Meng], Huang, J.H.Z.[Jian-Hua Z.], Qian, X.N.[Xiao-Ning],
Sparse exponential family Principal Component Analysis,
PR(60), No. 1, 2016, pp. 681-691.
Elsevier DOI 1609
Dimension reduction BibRef

Lu, G.F.[Gui-Fu], Zou, J.[Jian], Wang, Y.[Yong], Wang, Z.Q.[Zhong-Qun],
L1-norm-based principal component analysis with adaptive regularization,
PR(60), No. 1, 2016, pp. 901-907.
Elsevier DOI 1609
Principal component analysis BibRef

Yi, S.Y.[Shuang-Yan], Lai, Z.H.[Zhi-Hui], He, Z.Y.[Zhen-Yu], Cheung, Y.M.[Yiu-Ming], Liu, Y.[Yang],
Joint sparse principal component analysis,
PR(61), No. 1, 2017, pp. 524-536.
Elsevier DOI 1705
Dimensionality reduction Comment:
See also Comment on 'Joint sparse principal component analysis' by S. Yi et al. (Pattern Recognition, vol. 61, pp. 524-536, 2017). BibRef

Yi, S.Y.[Shuang-Yan], He, Z.Y.[Zhen-Yu], Li, Y.[Yi], Cheung, Y.M.[Yiu-Ming], Chen, W.S.[Wen-Sheng],
Simultaneous Dual-Views Reconstruction with Adaptive Dictionary and Low-Rank Representation,
ICPR16(1607-1611)
IEEE DOI 1705
Databases, Dictionaries, Feature extraction, Geometry, Linear programming, Optimization, Training BibRef

Forghani, Y.[Yahya],
Comment on 'Joint sparse principal component analysis' by S. Yi et al. (Pattern Recognition, vol. 61, pp. 524-536, 2017),
PR(77), 2018, pp. 454-455.
Elsevier DOI 1802
Joint sparse principal component analysis (JSPCA), Feature selection, Convergence, Local optimal solution
See also Joint sparse principal component analysis. BibRef

Lee, J., Choe, Y.,
Robust PCA Based on Incoherence With Geometrical Interpretation,
IP(27), No. 4, April 2018, pp. 1939-1950.
IEEE DOI 1802
blind source separation, computational complexity, principal component analysis, sparse matrices, source separation BibRef

de Pierrefeu, A., Löfstedt, T., Hadj-Selem, F., Dubois, M., Jardri, R., Fovet, T., Ciuciu, P., Frouin, V., Duchesnay, E.,
Structured Sparse Principal Components Analysis With the TV-Elastic Net Penalty,
MedImg(37), No. 2, February 2018, pp. 396-407.
IEEE DOI 1802
Loading, Minimization, Neuroimaging, Optimization, Principal component analysis, Sociology, TV, MRI, PCA, total variation, unsupervised machine learning BibRef

Minnehan, B.[Breton], Savakis, A.[Andreas],
Deep domain adaptation with manifold aligned label transfer,
MVA(30), No. 3, April 2019, pp. 473-485.
WWW Link. 1906
BibRef
Earlier:
Manifold Guided Label Transfer for Deep Domain Adaptation,
Diff-CVML17(744-752)
IEEE DOI 1709
Feature extraction, Manifolds, Measurement, Principal component analysis, Training BibRef

Kumar, S.[Sriram], Savakis, A.[Andreas],
Learning a perceptual manifold for image set classification,
ICIP16(4433-4437)
IEEE DOI 1610
BibRef
Earlier:
Robust Domain Adaptation on the L1-Grassmannian Manifold,
DIFF-CV16(1058-1065)
IEEE DOI 1612
Biologically motivated BibRef

Chen, X.H.[Xiu-Hong], Sun, H.Q.[Hui-Qiang],
L_2,1-norm-based sparse principle component analysis with trace norm regularised term,
IET-IPR(13), No. 6, 10 May 2019, pp. 910-922.
DOI Link 1906
BibRef

Ma, J.[Ji], Yuan, Y.Y.[Yu-Yu],
Dimension reduction of image deep feature using PCA,
JVCIR(63), 2019, pp. 102578.
Elsevier DOI 1909
Deep learning, Feature extraction, Dimension reduction, PCA algorithm BibRef

Li, X.,
Convolutional PCA for Multiple Time Series,
SPLetters(27), 2020, pp. 1450-1454.
IEEE DOI 2009
Principal component analysis, Frequency-domain analysis, Time series analysis, Convolution, Time-domain analysis, signal detection BibRef

He, Z.X.[Zai-Xing], Wu, M.T.[Meng-Tian], Zhao, X.Y.[Xin-Yue], Zhang, S.Y.[Shu-You], Tan, J.R.[Jian-Rong],
Representative null space LDA for discriminative dimensionality reduction,
PR(111), 2021, pp. 107664.
Elsevier DOI 2012
Linear discriminant analysis, Dimensionality reduction, Feature selection, Null space, Overfitting, Singularity problem BibRef

Nie, F.P.[Fei-Ping], Tian, L.[Lai], Huang, H.[Heng], Ding, C.[Chris],
Non-Greedy L21-Norm Maximization for Principal Component Analysis,
IP(30), 2021, pp. 5277-5286.
IEEE DOI 2106
Principal component analysis, Minimization, Covariance matrices, Robustness, Optimization, Convergence, Linear programming, L21-norm maximization BibRef

Lim, Y.J.[Yae-Ji], Kwon, J.[Junhyeon], Oh, H.S.[Hee-Seok],
Principal component analysis in the wavelet domain,
PR(119), 2021, pp. 108096.
Elsevier DOI 2108
Principal component analysis, Non-stationary time series, Wavelet process, Feature extraction, Seismic data BibRef

He, F.[Fan], Lv, K.[Kexin], Yang, J.[Jie], Huang, X.L.[Xiao-Lin],
One-Shot Distributed Algorithm for PCA With RBF Kernels,
SPLetters(28), 2021, pp. 1465-1469.
IEEE DOI 2108
Kernel, Principal component analysis, Signal processing algorithms, Partitioning algorithms, RBF kernels BibRef

Sofuoglu, S.E.[Seyyid Emre], Aviyente, S.[Selin],
Graph Regularized Low-Rank Tensor-Train for Robust Principal Component Analysis,
SPLetters(29), 2022, pp. 1152-1156.
IEEE DOI 2205
Tensors, Principal component analysis, Correlation, Optimization, Geometry, Matrix decomposition, Manifold learning, Tensors, robustness BibRef

Dhanaraj, M.[Mayur], Markopoulos, P.P.[Panos P.],
On the Asymptotic L1-PC of Elliptical Distributions,
SPLetters(29), 2022, pp. 2343-2347.
IEEE DOI 2212
Principal component analysis, Standards, Data models, Distributed databases, Wireless communication, Resistance, Elliptical Distribution BibRef

Lee, J.[Jongmin], Oh, H.S.[Hee-Seok],
Robust spherical principal curves,
PR(138), 2023, pp. 109380.
Elsevier DOI 2303
Principal curves are a nonlinear generalization of principal components. Dimension reduction, Robustness, Measure of central tendency, Spherical domain BibRef

Wang, Y.L.[Yu-Long], Kou, K.I.[Kit Ian], Chen, H.[Hong], Tang, Y.Y.[Yuan Yan], Li, L.Q.[Luo-Qing],
Double Auto-Weighted Tensor Robust Principal Component Analysis,
IP(32), 2023, pp. 5114-5125.
IEEE DOI 2310
BibRef

Migenda, N.[Nico], Möller, R.[Ralf], Schenck, W.[Wolfram],
Adaptive local Principal Component Analysis improves the clustering of high-dimensional data,
PR(146), 2024, pp. 110030.
Elsevier DOI 2311
High-dimensional clustering, Potential function, Adaptive learning rate, Ranking criteria, Local PCA BibRef

Zhang, C.[Chihao], Gai, K.[Kuo], Zhang, S.H.[Shi-Hua],
Matrix normal PCA for interpretable dimension reduction and graphical noise modeling,
PR(154), 2024, pp. 110591.
Elsevier DOI 2406
Principal component analysis, Dimension reduction, Matrix normal distribution, Sparse inverse covariance, Graphical noise modeling BibRef

Ju, F., Sun, Y., Gao, J., Liu, S., Hu, Y., Yin, B.,
Mixture of Bilateral-Projection Two-Dimensional Probabilistic Principal Component Analysis,
CVPR16(4462-4470)
IEEE DOI 1612
BibRef

Lu, C., Feng, J., Chen, Y., Liu, W., Lin, Z., Yan, S.,
Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization,
CVPR16(5249-5257)
IEEE DOI 1612
BibRef

Shi, F.Y.[Fei-Yu], Zhai, M.[Menghua], Duncan, D.[Drew], Jacobs, N.[Nathan],
MPCA: EM-based PCA for mixed-size image datasets,
ICIP14(1807-1811)
IEEE DOI 1502
BibRef
And: A2, A1, A3, A4:
Covariance-Based PCA for Multi-size Data,
ICPR14(1603-1608)
IEEE DOI 1412
Covariance matrices BibRef

Abdel-Hakim, A.E.[Alaa E.], El-Saban, M.[Motaz],
FRPCA: Fast Robust Principal Component Analysis for online observations,
ICPR12(413-416).
WWW Link. 1302
BibRef

Wang, S.J.[Su-Jing], Sun, M.F.[Ming-Fang], Chen, Y.H.[Yu-Hsin], Pang, E.P.[Er-Ping], Zhou, C.G.[Chun-Guang],
STPCA: Sparse tensor Principal Component Analysis for feature extraction,
ICPR12(2278-2281).
WWW Link. 1302
BibRef

Kawatani, T., Shimizu, H.,
Complementary Classifier Design Using Difference Principal Components,
ICDAR97(875-880).
IEEE DOI 9708
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Intrinsic Dimensionality .