Low-Rank and Sparse Modeling for Visual Analysis
Published on 1. October 2016
Book
Paperback/Softback
VII, 236 pages
978-3-319-35567-2 (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
Edition
Softcover reprint of the original 1st ed. 2014
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
51 farbige Abbildungen, 15 s/w Abbildungen
VII, 236 p. 66 illus., 51 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 14 mm
Weight
376 gr
ISBN-13
978-3-319-35567-2 (9783319355672)
DOI
10.1007/978-3-319-12000-3
Schweitzer Classification
Other editions
Additional editions
Book
11/2014
Springer
€106.99
Shipment within 10-15 days
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.