Low-Rank and Sparse Modeling for Visual Analysis

Name: Low-Rank and Sparse Modeling for Visual Analysis
Author: Yun Fu

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Gebonden, 236 blz. | Engels

Springer International Publishing | 2014e druk, 2014

ISBN13: 9783319119991

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Springer International Publishing 2014e druk, 2014 9783319119991

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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.

Specificaties

ISBN13:9783319119991

Taal:Engels

Bindwijze:gebonden

Aantal pagina's:236

Uitgever:Springer International Publishing

Druk:2014

Hoofdrubriek:Automatisering algemeen, Computer en informatica

Inhoudsopgave

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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