Low-Rank and Sparse Modeling for Visual Analysis
Published on 19. November 2014
Book
Hardback
VII, 236 pages
978-3-319-11999-1 (ISBN)
Description
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.
More details
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Research
Illustrations
15 s/w Abbildungen, 51 farbige Abbildungen
VII, 236 p. 66 illus., 51 illus. in color.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 18 mm
Weight
535 gr
ISBN-13
978-3-319-11999-1 (9783319119991)
DOI
10.1007/978-3-319-12000-3
Schweitzer Classification
Other editions
Additional editions
Book
10/2016
Springer
€106.99
Shipment within 10-15 days
E-Book
10/2014
1st Edition
Springer
€96.29
Available for download
Person
Yun Fu is an Assistant Professor, ECE and CS, Northeastern University
Content
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.