Yun Fu
Low-Rank and Sparse Modeling for Visual Analysis
Gebonden Engels 2014 2014e druk 9783319119991
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Samenvatting
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
Lezersrecensies
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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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