Low-Rank and Sparse Modeling for Visual Analysis
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.
Covers the most state-of-the-art topics of sparse and low-rank modelingExamines the theory of sparse and low-rank analysis to the real-world practice of sparse and low-rank analysisContributions from top experts voicing their unique perspectives included throughout