Subspace, Latent Structure and Feature Selection

Chapter 1 Discrete Component Analysis

Chapter 2 Overview and Recent Advances in Partial Least Squares

Chapter 3 Random Projection, Margins, Kernels, and Feature-Selection

Chapter 5 Feature Selection for Dimensionality Reduction

Chapter 6 Auxiliary Variational Information Maximization for Dimensionality Reduction

Chapter 7 Constructing Visual Models with a Latent Space Approach

Chapter 8 Is Feature Selection Still Necessary?

Chapter 9 Class-Specific Subspace Discriminant Analysis for High-Dimensional Data

Chapter 10 Incorporating Constraints and Prior Knowledge into Factorization Algorithms – An Application to 3D Recovery

Chapter 11 A Simple Feature Extraction for High Dimensional Image Representations

Chapter 12 Identifying Feature Relevance Using a Random Forest

Chapter 13 Generalization Bounds for Subspace Selection and Hyperbolic PCA

Chapter 14 Less Biased Measurement of Feature Selection Benefits