Low-Rank and Sparse Modeling for Visual Analysis, Softcover reprint of the original 1st ed. 2014

This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications.

Nonlinearly Structured Low-Rank Approximation.- Latent Low-Rank Representation.- Scalable Low-Rank Representation.- Low-Rank and Sparse Dictionary Learning.- Low-Rank Transfer Learning.- Sparse Manifold Subspace Learning.- Low Rank Tensor Manifold Learning.- Low-Rank and Sparse Multi-Task Learning.- Low-Rank Outlier Detection.- Low-Rank Online Metric Learning.

Yun Fu is an Assistant Professor, ECE and CS, Northeastern University

Covers the most state-of-the-art topics of sparse and low-rank modeling Examines the theory of sparse and low-rank analysis to the real-world practice of sparse and low-rank analysis Contributions from top experts voicing their unique perspectives included throughout Includes supplementary material: sn.pub/extras