13.4.1.5 Surveys, Comparisons, Evaluations, Principal Components

Gerbrands, J.J.[Jan J.],
On the relationships between SVD, KLT and PCA,
PR(14), No. 1-6, 1981, pp. 375-381.
WWW Version. 0309 BibRef

Zhao, L.[Li], Yang, Y.H.[Yee-Hong],
Theoretical analysis of illumination in PCA-based vision systems,
PR(32), No. 4, April 1999, pp. 547-564.
WWW Version. BibRef 9904

Martínez, A.M.[Aleix M.], Kak, A.C.[Avinash C.],
PCA versus LDA,
PAMI(23), No. 2, February 2001, pp. 228-233.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0102When the training set is small, PCA (Principal Components Analysis) outperforms LDA (Linear Discriminant Analysis) and is less sensitive to different training sets. Applied to faces with occlusions. BibRef

Ramamoorthi, R.[Ravi],
Analytic PCA Construction for Theoretical Analysis of Lighting Variability in Images of a Lambertian Object,
PAMI(24), No. 10, October 2002, pp. 1322-1333.
IEEE Abstract. IEEE Top Reference. 0210Analysis of the process by changing lighting. BibRef

Guillamet, D., Vitriŕ, J.,
Evaluation of distance metrics for recognition based on non-negative matrix factorization,
PRL(24), No. 9-10, June 2003, pp. 1599-1605.
WWW Version. 0304 BibRef
Earlier:
Determining a suitable metric when using non-negative matrix factorization,
ICPR02(II: 128-131).
WWW Version. 0211 BibRef

Guillamet, D., Vitriŕ, J., Schiele, B.,
Introducing a weighted non-negative matrix factorization for image classification,
PRL(24), No. 14, October 2003, pp. 2447-2454.
WWW Version. 0307 BibRef
Earlier: A1, A3, A2:
Analyzing non-negative matrix factorization for image classification,
ICPR02(II: 116-119).
WWW Version. 0211 BibRef

Guillamet, D., Vitria, J.,
Discriminant basis for object classification,
CIAP01(256-261).
IEEE Top Reference. 0210 BibRef

Guillamet, D.[David], Bressan, M.[Marco], and Vitriŕ, J.[Jordi],
A Weighted Non-negative Matrix Factorization for Local Representations,
CVPR01(I:942-947).
IEEE Abstract. IEEE Top Reference. 0110Deal with problems of the original formulation to get better representations. BibRef

Bressan, M.[Marco], Guillamet, D.[David], and Vitriŕ, J.[Jordi],
Using an ICA Representation of High Dimensional Data for Object Recognition and Classification,
CVPR01(I:1004-1009).
IEEE Abstract. IEEE Top Reference. 0110 BibRef

Bressan, M.[Marco], Vitriŕ, J.[Jordi],
Independent Modes of Variation in Point Distribution Models,
VF01(123 ff.).
HTML Version. 0209 BibRef

Guillamet, D., Moghaddam, B., Vitria, J.,
Higher-order dependencies in local appearance models,
ICIP03(I: 213-216).
IEEE Abstract. IEEE Top Reference. 0312 BibRef
Earlier: A2, A1, A3:
Local appearance-based models using high-order statistics of image features,
CVPR03(I: 729-735).
IEEE Abstract. IEEE Top Reference. 0307 BibRef

Wang, L.W.[Li-Wei], Wang, X.[Xiao], Zhang, X.R.[Xue-Rong], Feng, J.[Jufu],
The equivalence of two-dimensional PCA to line-based PCA,
PRL(26), No. 1, 1 January 2005, pp. 57-60.
WWW Version. 0501 BibRef

Martínez, A.M.[Aleix M.], Zhu, M.L.[Man-Li],
Where Are Linear Feature Extraction Methods Applicable?,
PAMI(27), No. 12, December 2005, pp. 1934-1944.
WWW Version. 0512Analyze where and why eigen-based linear equations do not work. When the smallest angle between the ith eigenvector and the first i eigenvectors is close to zero, there are problems. BibRef

Gao, H.[Hui], Davis, J.W.[James W.],
Why direct LDA is not equivalent to LDA,
PR(39), No. 5, May 2006, pp. 1002-1006.
WWW Version. 0604 BibRef
Earlier:
Sampling Representative Examples for Dimensionality Reduction and Recognition: Bumping LDA,
ECCV06(III: 275-287).
WWW Version. 0608Linear discriminant analysis; Direct LDA; Small sample size problem BibRef

Chen, P.[Pei], Suter, D.[David],
An Analysis of Linear Subspace Approaches for Computer Vision and Pattern Recognition,
IJCV(68), No. 1, June 2006, pp. 83-106.
WWW Version. 0605Such as PCA or SVD. BibRef

Bethge, M.[Matthias],
Factorial coding of natural images: how effective are linear models in removing higher-order dependencies?,
JOSA-A(23), No. 6, June 2006, pp. 1253-1268.
WWW Version. 0610 BibRef

Vicente, M.A.[M. Asuncion], Hoyer, P.O.[Patrik O.], Hyvarinen, A.[Aapo],
Equivalence of Some Common Linear Feature Extraction Techniques for Appearance-Based Object Recognition Tasks,
PAMI(29), No. 5, May 2007, pp. 896-900.
WWW Version. 0704Contradictory evaluations of PCA vs. ICA. Whitened PCA may yield identical results to ICA in some cases. Describe the situations where ICA improves on PCA. BibRef

Gao, Q.X.[Quan-Xue],
Is two-dimensional PCA equivalent to a special case of modular PCA?,
PRL(28), No. 10, 15 July 2007, pp. 1250-1251.
WWW Version. 0706Modular PCA; Two-dimensional PCA BibRef

Shih, F.Y.[Frank Y.], Zhang, K.[Kai],
A distance-based separator representation for pattern classification,
IVC(26), No. 5, May 2008, pp. 667-672.
WWW Version. 0803Pattern representation; Classification; Support vector machine; PCA; LDA BibRef

Zheng, W.S.[Wei-Shi], Lai, J.H.[Jian-Huang], Li, S.Z.[Stan Z.],
1D-LDA vs. 2D-LDA: When is vector-based linear discriminant analysis better than matrix-based?,
PR(41), No. 7, July 2008, pp. 2156-2172.
WWW Version. 0804Fisher's linear discriminant analysis (LDA); Matrix-based representation; Vector-based representation; Pattern recognition BibRef

Salgian, A.S.[Andrea Selinger],
Using Multiple Patches for 3D Object Recognition,
BP07(1-6).
WWW Version. 0706 BibRef
Earlier:
Object Recognition Using Local Descriptors: A Comparison,
ISVC06(II: 709-717).
WWW Version. 0611Build on: See also Scale and Affine Invariant Interest Point Detectors. SIFT ( See also Distinctive Image Features from Scale-Invariant Keypoints. ), PCA-SIFT ( See also PCA-SIFT: a more distinctive representation for local image descriptors. ) and keyed context patches ( See also Perceptual Grouping Hierarchy for Appearance-Based 3D Object Recognition, A. ). BibRef

Choksuriwong, A., Laurent, H., Emile, B.,
Comparison of Invariant Descriptors for Object Recognition,
ICIP05(I: 377-380).
WWW Version. 0512 BibRef

Vicente, M.A.[M. Asunción], Fernández, C.[Cesar], Reinoso, O.[Oscar], Payá, L.[Luis],
3D Object Recognition from Appearance: PCA Versus ICA Approaches,
ICIAR04(I: 547-555).
WWW Version. 0409 BibRef

Brand, M.,
From Subspace to Submanifold Methods,
BMVC04(xx-yy).
HTML Version. 0508 BibRef

Lenz, R., Bui, T.H.[Thanh Hai],
Recognition of non-negative patterns,
ICPR04(III: 498-501).
WWW Version. 0409PCA analysis. Prove that the non-negative vauues are right. BibRef

Fortuna, J., Quick, P., Capson, D.W.,
A comparison of subspace methods for accurate position measurement,
Southwest04(16-20).
IEEE Abstract. IEEE Top Reference. 0411 BibRef

Fortuna, J., Schuurman, D.C., Capson, D.W.,
A comparison of PCA and ICA for object recognition under varying illumination,
ICPR02(III: 11-15).
WWW Version. 0211 BibRef

Fortuna, J.[Jeff], Capson, D.W.[David W.],
Improved support vector classification using PCA and ICA feature space modification,
PR(37), No. 6, June 2004, pp. 1117-1129.
WWW Version. 0405 BibRef
And:
ICA filters for lighting invariant face recognition,
ICPR04(I: 334-337).
WWW Version. 0409 BibRef

Wu, Q., Liu, Z., Xiong, Z., Wang, Y., Chen, T., Castleman, K.R.,
On optimal subspaces for appearance-based object recognition,
ICIP02(III: 885-888).
IEEE Abstract. IEEE Top Reference. 0210 BibRef

Fancourt, C.L.[Craig L.], Principe, J.C.[Jose C.],
Soft Competitive Principal Component Analysis Using the Mixture of Experts,
DARPA97(1071-1076). BibRef 9700

Pedersen, F.[Finn], Andersson, L.[Leif], and Bengtsson, E.[Ewert],
Investigating Preprocessing of Multivariate Images in Combination with Principal Component Analysis,
SCIA97(xx-yy) 9705
HTML Version. BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Learning for Principal Components, Eigen Representations .