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Table of Contents

• Introduction
• Linear Models (Single Subspace Models, Multiple-Subspace Models, Theoretical Analysis)
• Non-Linear Models (Kernel Methods, Laplacian and Hyper-Laplacian Methods, Locally Linear Representation, Transformation Invariant Clustering)
• Optimization Algorithms (Convex Algorithms, Non-Convex Algorithms, Randomized Algorithms)
• Representative Applications (Video Denoising, Background Modeling, Robust Alignment by Sparse and Low-Rank Decomposition, Transform Invariant Low-Rank Textures, Motion and Image Segmentation, Image Saliency Detection, Partial-Duplicate Image Search, Image Tag Completion and Refinement, Other Applications)
• Conclusions (Low-Rank Models for Tensorial Data, Nonlinear Manifold Clustering, Randomized Algorithms)