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Subspace, Latent Structure and Feature Selection

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Cover of 'Subspace, Latent Structure and Feature Selection'

Table of Contents

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    Book Overview
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    Chapter 1 Discrete Component Analysis
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    Chapter 2 Overview and Recent Advances in Partial Least Squares
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    Chapter 3 Random Projection, Margins, Kernels, and Feature-Selection
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    Chapter 4 Subspace, Latent Structure and Feature Selection
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    Chapter 5 Feature Selection for Dimensionality Reduction
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    Chapter 6 Auxiliary Variational Information Maximization for Dimensionality Reduction
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    Chapter 7 Constructing Visual Models with a Latent Space Approach
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    Chapter 8 Is Feature Selection Still Necessary?
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    Chapter 9 Class-Specific Subspace Discriminant Analysis for High-Dimensional Data
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    Chapter 10 Incorporating Constraints and Prior Knowledge into Factorization Algorithms – An Application to 3D Recovery
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    Chapter 11 A Simple Feature Extraction for High Dimensional Image Representations
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    Chapter 12 Identifying Feature Relevance Using a Random Forest
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    Chapter 13 Generalization Bounds for Subspace Selection and Hyperbolic PCA
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    Chapter 14 Less Biased Measurement of Feature Selection Benefits
Attention for Chapter 6: Auxiliary Variational Information Maximization for Dimensionality Reduction
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