Low-Rank and Sparse Modeling for Visual Analysis
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Springer International Publishing
Naturwissenschaften, Medizin, Informatik, Technik / Anwendungs-Software
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. 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.
Kundenbewertungen
Compressive Sensing, Low-Rank Recover, Machine Learning, Dimensionality Reduction, Computer Vision, Low-Rank Approximation, Subspace Learning, Sparse Representation, Low-Rank Representation, Pattern Recognition