Low-Rank and Sparse Modeling for Visual Analysis
| Autor: | Yun Fu |
|---|---|
| EAN: | 9783319120003 |
| eBook Format: | |
| Sprache: | Englisch |
| Produktart: | eBook |
| Veröffentlichungsdatum: | 30.10.2014 |
| Kategorie: | |
| Schlagworte: | Compressive Sensing Computer Vision Dimensionality Reduction Low-Rank Approximation Low-Rank Recover Low-Rank Representation Machine Learning Pattern Recognition Sparse Representation Subspace Learning |
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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.
Yun Fu is an Assistant Professor, ECE and CS, Northeastern University
Yun Fu is an Assistant Professor, ECE and CS, Northeastern University
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