13.4.1.7 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.
WWW Version. 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 Link 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,
J. Neural Syst.(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. 9712

PDF Version. BibRef
Earlier: TRUniv. of California Santa Barbara. 1995. BibRef
And: A2, A1, A3 Only: SCV95(551-556).
IEEE Top Reference.
PDF Version. Introduce the vision community to the eigenspace updating algoithms from signal processing.
HTML Version. BibRef

Rajan, J.J., Rayner, P.J.W.,
Model Order Selection for the Singular-Value Decomposition and the Discrete Karhunen-Loeve Transform Using a Bayesian-Approach,
VISP(144), No. 2, April 1997, pp. 116-123. 9706
BibRef

Boyle, R.D.[Roger D.],
Scaling additional contributions to principal components analysis,
PR(31), No. 12, December 1998, pp. 2047-2053.
WWW Version. 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.
WWW Version. 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.
WWW Version. 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 Link 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 Abstract. IEEE Top Reference.
WWW Version. 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.
WWW Version. 0212
BibRef
Earlier:
Adding and subtracting eigenspaces,
BMVC99(Models and Search).
PDF Version. BibRef

Bach, F.R., and Jordan, M.I.,
Kernel independent component analysis,
MachLearnRes(3), 2002, pp. 1-48.
WWW Version. 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 Link 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 Link 0606
BibRef

Saitwal, K., Maciejewski, A.A., Roberts, R.G.,
Eigendecomposition of Correlated Images Characterized by Three Parameters,
Southwest06(203-207).
IEEE DOI Link 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.
WWW Version. 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. IEEE Top Reference. 0401
PCA applied to a 2-D matrix rather than conversion to 1-D matrix. 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.
WWW Version. 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. IEEE Top Reference. 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).
WWW Version. 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.
WWW Version. 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 Link 0606
BibRef
Earlier: A1, A3, A2:
Bi-Directional PCA with Assembled Matrix Distance Metric,
ICIP05(II: 958-961).
IEEE DOI Link 0512
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.
HTML Version. 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.
WWW Version. 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.
WWW Version. 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.
WWW Version. 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.
WWW Version. 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 Link 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 Link 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. IEEE Top Reference. 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. IEEE Top Reference. 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. IEEE Top Reference. 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.
WWW Version. 0405
BibRef
And: Erratum: PRL(26), No. 14, 15 October 2005, pp. 2313.
WWW Version. 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).
WWW Version. 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. IEEE Top Reference. 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.
WWW Version. 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. IEEE Top Reference. 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.
WWW Version. 0505
BibRef

Lipovetsky, S.[Stan],
PCA and SVD with nonnegative loadings,
PR(42), No. 1, January 2009, pp. 68-76.
WWW Version. 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.A.[Dao-Qi-Ang], Yang, J.Y.[Jing-Yu],
Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA,
PRL(26), No. 8, June 2005, pp. 1157-1167.
WWW Version. 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 Link 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.
WWW Version. 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 Link 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 Link 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 Link 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.
WWW Version. 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 Version. 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 Link 0905
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.
WWW Version. 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 Link 0706
BibRef
Earlier:
Improving the Speed of Kernel PCA on Large Scale Datasets,
AVSBS06(41-41).
IEEE DOI Link 0611
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.
WWW Version. 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.
WWW Version. 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.
WWW Version. 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.
WWW Version. 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.
WWW Version. 0510
BibRef

Pang, Y., Tao, D., Yuan, Y., Li, X.,
Binary Two-Dimensional PCA,
SMC-B(37), No. 4, August 2008, pp. 1176-1180.
IEEE DOI Link 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.
WWW Version. 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 Link 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 Link
WWW Version. 0911
Principal component analysis; Incremental PCA; Combining collections BibRef

Yang, Z.R.[Zhi-Rong], Laaksonen, J.T.[Jorma T.],
Informative Laplacian Projection,
SCIA09(359-368).
Springer DOI Link 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 Link 0906
Formulate problems as minimization problem to solve using SVD, which does not work well with missing data. BibRef

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

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

Gurumoorthy, K.S.[Karthik S.], Rajwade, A.[Ajit], Banerjee, A.[Arunava], Rangarajan, A.[Anand],
Beyond SVD: Sparse projections onto exemplar orthonormal bases for compact image representation,
ICPR08(1-4).
IEEE DOI Link 0812
BibRef

Mazhar, R.[Raazia], Gader, P.D.[Paul D.],
EK-SVD: Optimized dictionary design for sparse representations,
ICPR08(1-4).
IEEE DOI Link 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 Link 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 Link 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 Link 0810
BibRef
And:
An EM algorithm for robust Bayesian PCA with student's t-distribution,
ICIP08(2672-2675).
IEEE DOI Link 0810
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 Link 0906
BibRef
Earlier:
Normalization and preimage problem in gaussian kernel PCA,
ICIP08(741-744).
IEEE DOI Link 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 Link 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 Link 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 Link 0806
BibRef

Shi, Y.G.[Yong-Gang], Lai, R.[Rongjie], Krishna, S.[Sheila], Sicotte, N.[Nancy], Dinov, I.[Ivo], Toga, A.W.[Arthur W.],
Anisotropic Laplace-Beltrami eigenmaps: Bridging Reeb graphs and skeletons,
MMBIA08(1-7).
IEEE DOI Link 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 Link 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 Link 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 Link 0809
BibRef
Earlier:
Joint Diagonalization of Kernels for Information Fusion,
CIARP07(556-563).
Springer DOI Link 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 Link 0709
BibRef

Aiello, M., Andreozzi, F., Catanzariti, E., Isgro, F., Santoro, M.,
Fast convergence for spectral clustering,
CIAP07(641-646).
IEEE DOI Link 0709
Cluster using first few eigen vectors. 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 Link 0706
BibRef

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

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

Yan, S.C.[Shui-Cheng], Tang, X.[Xiaoou],
Largest-Eigenvalue-Theory for Incremental Principal Component Analysis,
ICIP05(I: 1181-1184).
IEEE DOI Link 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 Link 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 Link 0801
BibRef
Earlier:
Rank-R Approximation of Tensors: Using Image-as-Matrix Representation,
CVPR05(II: 346-353).
IEEE DOI Link 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 Link 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 Link 0506
BibRef

Mühlich, M.[Matthias], Mester, R.[Rudolf],
Unbiased Errors-In-Variables Estimation Using Generalized Eigensystem Analysis,
SMVP04(38-49).
WWW Version. 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).
WWW Version. 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 Abstract. IEEE Top Reference. 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 Abstract. IEEE Top Reference. 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 Link 0709
BibRef

Olshausen, B.A.,
Learning sparse, overcomplete representations of time-varying natural images,
ICIP03(I: 41-44).
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Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Faces using Invariants -- Eigenfaces .