Low-Rank and Sparse Modeling for Visual Analysis (inbunden)
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Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
236
Utgivningsdatum
2014-11-19
Upplaga
2014 ed.
Förlag
Springer International Publishing AG
Medarbetare
Fu, Yun (ed.)
Illustratör/Fotograf
32 schwarz-weiße Tabellen 15 schwarz-weiße und 51 farbige Abbildungen Bibliographie
Illustrationer
51 Illustrations, color; 15 Illustrations, black and white; VII, 236 p. 66 illus., 51 illus. in colo
Dimensioner
234 x 156 x 16 mm
Vikt
522 g
Antal komponenter
1
Komponenter
1 Hardback
ISBN
9783319119991

Low-Rank and Sparse Modeling for Visual Analysis

av Yun Fu
Inbunden,  Engelska, 2014-11-19
1505
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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.
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Övrig information

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

Innehållsförteckning

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.

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