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Discriminant feature extraction: exploiting structures within each sample and across samples.

Zhang, Wei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 95-109). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Area of Machine Learning --- p.1 / Chapter 1.1.1 --- Types of Algorithms --- p.2 / Chapter 1.1.2 --- Modeling Assumptions --- p.4 / Chapter 1.2 --- Dimensionality Reduction --- p.4 / Chapter 1.3 --- Structure of the Thesis --- p.8 / Chapter 2 --- Dimensionality Reduction --- p.10 / Chapter 2.1 --- Feature Extraction --- p.11 / Chapter 2.1.1 --- Linear Feature Extraction --- p.11 / Chapter 2.1.2 --- Nonlinear Feature Extraction --- p.16 / Chapter 2.1.3 --- Sparse Feature Extraction --- p.19 / Chapter 2.1.4 --- Nonnegative Feature Extraction --- p.19 / Chapter 2.1.5 --- Incremental Feature Extraction --- p.20 / Chapter 2.2 --- Feature Selection --- p.20 / Chapter 2.2.1 --- Viewpoint of Feature Extraction --- p.21 / Chapter 2.2.2 --- Feature-Level Score --- p.22 / Chapter 2.2.3 --- Subset-Level Score --- p.22 / Chapter 3 --- Various Views of Feature Extraction --- p.24 / Chapter 3.1 --- Probabilistic Models --- p.25 / Chapter 3.2 --- Matrix Factorization --- p.26 / Chapter 3.3 --- Graph Embedding --- p.28 / Chapter 3.4 --- Manifold Learning --- p.28 / Chapter 3.5 --- Distance Metric Learning --- p.32 / Chapter 4 --- Tensor linear Laplacian discrimination --- p.34 / Chapter 4.1 --- Motivation --- p.35 / Chapter 4.2 --- Tensor Linear Laplacian Discrimination --- p.37 / Chapter 4.2.1 --- Preliminaries of Tensor Operations --- p.38 / Chapter 4.2.2 --- Discriminant Scatters --- p.38 / Chapter 4.2.3 --- Solving for Projection Matrices --- p.40 / Chapter 4.3 --- Definition of Weights --- p.44 / Chapter 4.3.1 --- Contextual Distance --- p.44 / Chapter 4.3.2 --- Tensor Coding Length --- p.45 / Chapter 4.4 --- Experimental Results --- p.47 / Chapter 4.4.1 --- Face Recognition --- p.48 / Chapter 4.4.2 --- Texture Classification --- p.50 / Chapter 4.4.3 --- Handwritten Digit Recognition --- p.52 / Chapter 4.5 --- Conclusions --- p.54 / Chapter 5 --- Semi-Supervised Semi-Riemannian Metric Map --- p.56 / Chapter 5.1 --- Introduction --- p.57 / Chapter 5.2 --- Semi-Riemannian Spaces --- p.60 / Chapter 5.3 --- Semi-Supervised Semi-Riemannian Metric Map --- p.61 / Chapter 5.3.1 --- The Discrepancy Criterion --- p.61 / Chapter 5.3.2 --- Semi-Riemannian Geometry Based Feature Extraction Framework --- p.63 / Chapter 5.3.3 --- Semi-Supervised Learning of Semi-Riemannian Metrics --- p.65 / Chapter 5.4 --- Discussion --- p.72 / Chapter 5.4.1 --- A General Framework for Semi-Supervised Dimensionality Reduction --- p.72 / Chapter 5.4.2 --- Comparison to SRDA --- p.74 / Chapter 5.4.3 --- Advantages over Semi-supervised Discriminant Analysis --- p.74 / Chapter 5.5 --- Experiments --- p.75 / Chapter 5.5.1 --- Experimental Setup --- p.76 / Chapter 5.5.2 --- Face Recognition --- p.76 / Chapter 5.5.3 --- Handwritten Digit Classification --- p.82 / Chapter 5.6 --- Conclusion --- p.84 / Chapter 6 --- Summary --- p.86 / Chapter A --- The Relationship between LDA and LLD --- p.89 / Chapter B --- Coding Length --- p.91 / Chapter C --- Connection between SRDA and ANMM --- p.92 / Chapter D --- From S3RMM to Graph-Based Approaches --- p.93 / Bibliography --- p.95

Identiferoai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_326912
Date January 2009
ContributorsZhang, Wei., Chinese University of Hong Kong Graduate School. Division of Information Engineering.
Source SetsThe Chinese University of Hong Kong
LanguageEnglish, Chinese
Detected LanguageEnglish
TypeText, bibliography
Formatprint, xiv, 109 leaves : ill. ; 30 cm.
RightsUse of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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