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Low-rank and sparse modeling for visual analysis

Author: Yun Fu
Publisher: Cham : Springer, 2014.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Printed edition:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Yun Fu
ISBN: 9783319120003 331912000X 3319119990 9783319119991
OCLC Number: 894509253
Notes: Includes index.
Description: 1 online resource (vii, 236 pages) : illustrations (some color)
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.
Responsibility: Yun Fu, editor.

Abstract:

This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data.  Read more...

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