Preface; Contents; 1 Introduction; 1.1 What Are Robust Data Representations?; 1.2 Organization of the Book; Part I Robust Representation Models; 2 Fundamentals of Robust Representations; 2.1 Representation Learning Models; 2.1.1 Subspace Learning; 2.1.2 Multi-view Subspace Learning; 2.1.3 Dictionary Learning; 2.2 Robust Representation Learning; 2.2.1 Subspace Clustering; 2.2.2 Low-Rank Modeling; References; 3 Robust Graph Construction; 3.1 Overview; 3.2 Existing Graph Construction Methods; 3.2.1 Unbalanced Graphs and Balanced Graph; 3.2.2 Sparse Representation Based Graphs

3.2.3 Low-Rank Learning Based Graphs3.3 Low-Rank Coding Based Unbalanced Graph Construction; 3.3.1 Motivation; 3.3.2 Problem Formulation; 3.3.3 Optimization; 3.3.4 Complexity Analysis; 3.3.5 Discussions; 3.4 Low-Rank Coding Based Balanced Graph Construction; 3.4.1 Motivation and Formulation; 3.4.2 Optimization; 3.5 Learning with Graphs; 3.5.1 Graph Based Clustering; 3.5.2 Transductive Semi-supervised Classification; 3.5.3 Inductive Semi-supervised Classification; 3.6 Experiments; 3.6.1 Databases and Settings; 3.6.2 Spectral Clustering with Graph

3.6.3 Semi-supervised Classification with Graph3.6.4 Discussions; 3.7 Summary; References; 4 Robust Subspace Learning; 4.1 Overview; 4.2 Supervised Regularization Based Robust Subspace (SRRS); 4.2.1 Problem Formulation; 4.2.2 Theoretical Analysis; 4.2.3 Optimization; 4.2.3.1 Learn Subspace P on Fixed Low-Rank Representations; 4.2.3.2 Learn Low-Rank Representations Z on Fixed Subspace; 4.2.4 Algorithm and Discussions; 4.3 Experiments; 4.3.1 Object Recognition with Pixel Corruption; 4.3.2 Face Recognition with Illumination and Pose Variation; 4.3.3 Face Recognition with Occlusions

4.3.4 Kinship Verification4.3.5 Discussions; 4.4 Summary; References; 5 Robust Multi-view Subspace Learning; 5.1 Overview; 5.2 Problem Definition; 5.3 Multi-view Discriminative Bilinear Projection (MDBP); 5.3.1 Motivation; 5.3.2 Formulation of MDBP; 5.3.2.1 Learning Shared Representations Across Views; 5.3.2.2 Incorporating Discriminative Regularization; 5.3.2.3 Modeling Temporal Smoothness; 5.3.2.4 Objective Function; 5.3.3 Optimization Algorithm; 5.3.3.1 Time Complexity Analysis; 5.3.4 Comparison with Existing Methods; 5.4 Experiments; 5.4.1 UCI Daily and Sports Activity Dataset

5.4.1.1 Two-View Setting5.4.1.2 Baselines; 5.4.1.3 Classification Scheme; 5.4.1.4 Results; 5.4.2 Multimodal Spoken Word Dataset; 5.4.2.1 Three-View Setting; 5.4.2.2 Results; 5.4.3 Discussions; 5.4.3.1 Parameter Sensitivity and Convergence; 5.4.3.2 Experiments with Data Fusion and Feature Fusion; 5.5 Summary; References; 6 Robust Dictionary Learning; 6.1 Overview; 6.2 Self-Taught Low-Rank (S-Low) Coding ; 6.2.1 Motivation; 6.2.2 Problem Formulation; 6.2.3 Optimization; 6.2.4 Algorithm and Discussions; 6.3 Learning with S-Low Coding; 6.3.1 S-Low Clustering; 6.3.2 S-Low Classification