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When the training set is small, PCA (Principal Components Analysis)
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0512
Analyze where and why eigen-based linear equations do not work.
When the smallest angle between the ith eigenvector
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Sampling Representative Examples for Dimensionality Reduction and
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Contradictory evaluations of PCA vs. ICA.
Whitened PCA may yield identical results to ICA in some cases.
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Pattern representation; Classification; Support vector machine; PCA; LDA
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Fisher's linear discriminant analysis (LDA); Matrix-based representation;
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ICPR08(1-4).
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Using Multiple Patches for 3D Object Recognition,
BP07(1-6).
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Earlier:
Object Recognition Using Local Descriptors: A Comparison,
ISVC06(II: 709-717).
Springer DOI Link
0611
Build 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. ).
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Laurent, H.,
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ICIP05(I: 377-380).
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Wu, Q.,
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Fancourt, C.L.[Craig L.],
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Soft Competitive Principal Component Analysis Using
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Pedersen, F.[Finn],
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Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Learning for Principal Components, Eigen Representations .