Low-Rank and Sparse Modeling for Visual Analysis
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Beschreibung
This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding, and learning among unconstrained visual data. Included in the book are chapters covering multiple emerging topics in this new field. The text links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. This book 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 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
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 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
Autor*in
Yun Fu
Themen in »Low-Rank and Sparse Modeling for Visual Analysis«
Compressive Sensing Computer Vision Dimensionality Reduction Low-Rank Approximation Low-Rank Recover Low-Rank Representation Machine Learning Pattern Recognition Sparse Representation Subspace Learning