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Low-Rank and Sparse Modeling for Visual Analysis [electronic resource] / edited by Yun Fu.

Contributor(s): Fu, Yun [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: VII, 236 p. 66 illus., 51 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319120003.Subject(s): Computer science | Computer graphics | Image processing | Computer Science | Image Processing and Computer Vision | Signal, Image and Speech Processing | Computer Imaging, Vision, Pattern Recognition and GraphicsAdditional physical formats: Printed edition:: No titleDDC classification: 006.6 | 006.37 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding, and learning among unconstrained visual data. Included in the book are chapters covering multiple emerging topics in this new field. The text links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. This book contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications. �         Covers the most state-of-the-art topics of sparse and low-rank modeling �         Examines the theory of sparse and low-rank analysis to the real-world practice of sparse and low-rank analysis �         Contributions from top experts voicing their unique perspectives included throughout.
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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.

This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding, and learning among unconstrained visual data. Included in the book are chapters covering multiple emerging topics in this new field. The text links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. This book contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications. �         Covers the most state-of-the-art topics of sparse and low-rank modeling �         Examines the theory of sparse and low-rank analysis to the real-world practice of sparse and low-rank analysis �         Contributions from top experts voicing their unique perspectives included throughout.

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