13.4.1.2 Invariants -- Principal Component Analysis

Jolliffe, I.T.,
Principal Component Analysis,
Springer-VerlagNew-York, 1986. Survey, PCA. The Book, overview. BibRef 8600

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

Wold, S.[Svante],
Pattern recognition by means of disjoint principal components models,
PR(8), No. 3, July 1976, pp. 127-139.
WWW Version. 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 Link 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.
WWW Version. 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.
WWW Version. 0210
BibRef

Glendinning, R.H., Herbert, R.A.,
Shape classification using smooth principal components,
PRL(24), No. 12, August 2003, pp. 2021-2030.
WWW Version. 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.
WWW Version.
PDF Version. 0309
BibRef
Earlier:
Robust Parameterized Component Analysis,
ECCV02(IV: 653 ff.).
HTML Version. 0205
BibRef
Earlier:
Robust Principal Component Analysis for Computer vision,
ICCV01(I: 362-369).
IEEE DOI Link 0106
BibRef
And:
Dynamic Coupled Component Analysis,
CVPR01(II:643-650).
IEEE Abstract. IEEE Top Reference. 0110
BibRef

de la Torre, F., Kanade, T.,
Oriented Discriminant Analysis,
BMVC04(xx-yy).
HTML Version. 0508
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.
WWW Version. 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 Link 0512
BibRef
Earlier: CVPR03(I: 621-628).
IEEE Abstract. IEEE Top Reference. 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 Abstract. IEEE Top Reference. 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.
WWW Version. 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 Link 0601
BibRef

Shekar, B.H., Guru, D.S., Nagabhushan, P.,
Two-Dimensional Optimal Transform for Appearance Based Object Recognition,
ICCVGIP06(650-661).
Springer DOI Link 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.
WWW Version. 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.
WWW Version. 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.
WWW Version. 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.
WWW Version. 0706
Fast PCA; Eigenvalue decomposition; Mean squared error 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 Link 0606
BibRef
Earlier:
Classification probability analysis of principal component null space analysis,
ICPR04(I: 240-243).
IEEE DOI Link 0409
BibRef

Wu, F.C., Hu, Z.Y.,
The LLE and a linear mapping,
PR(39), No. 9, September 2006, pp. 1799-1804.
WWW Version. 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.
WWW Version. 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 Link 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.
WWW Version. 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.
WWW Version. 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 Link 0806
BibRef
Earlier:
Shape-Based Approach to Robust Image Segmentation using Kernel PCA,
CVPR06(I: 977-984).
IEEE DOI Link 0606
BibRef
And:
A Shape-Based Approach to Robust Image Segmentation,
ICIAR06(I: 173-183).
Springer DOI Link 0610
BibRef
And: A2, A1, A3:
Comparative Analysis of Kernel Methods for Statistical Shape Learning,
CVAMIA06(96-107).
Springer DOI Link 0605
BibRef

Majumdar, A.,
Image compression by sparse PCA coding in curvelet domain,
SIViP(3), No. 1, January 2009, pp. xx-yy.
Springer DOI Link 0902
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 Link 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 Link 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). 0610

IEEE DOI Link BibRef

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

IEEE DOI Link 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 Link 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 Link 0509
BibRef

Jin, Z.[Zhong], Davoine, F.[Franck],
Orthogonal ICA representation of images,
ICARCV04(I: 369-374).
IEEE DOI Link 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 Link 0505
BibRef

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

Ke, Y.[Yan], Sukthankar, R.,
PCA-SIFT: a more distinctive representation for local image descriptors,
CVPR04(II: 506-513).
IEEE Abstract. IEEE Top Reference. 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).
WWW Version. 0405
BibRef

Le Bihan, N., Sangwine, S.J.,
Quaternion principal component analysis of color images,
ICIP03(I: 809-812).
IEEE Abstract. IEEE Top Reference. 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 Link 0211
BibRef

Hegazy, D.[Doaa], Denzler, J.[Joachim],
Generic Object Recognition Using Boosted Combined Features,
RobVis08(355-366).
Springer DOI Link 0802
BibRef

Drexler, C., Mattern, F., Denzler, J.,
Appearance Based Generic Object Modeling and Recognition Using Probabilistic Principal Component Analysis,
DAGM02(100 ff.).
HTML Version. 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 Link
HTML Version. 0009
BibRef

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

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