Low-Rank and Sparse Modeling for Visual Analysis

✍ Scribed by Yun Fu (eds.)

• Publisher: Springer International Publishing
• Year: 2014
• Tongue: English
• Leaves: 240
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-vii
Nonlinearly Structured Low-Rank Approximation....Pages 1-22
Latent Low-Rank Representation....Pages 23-38
Scalable Low-Rank Representation....Pages 39-60
Low-Rank and Sparse Dictionary Learning....Pages 61-85
Low-Rank Transfer Learning....Pages 87-115
Sparse Manifold Subspace Learning....Pages 117-132
Low Rank Tensor Manifold Learning....Pages 133-150
Low-Rank and Sparse Multi-task Learning....Pages 151-180
Low-Rank Outlier Detection....Pages 181-202
Low-Rank Online Metric Learning....Pages 203-233
Back Matter....Pages 235-236

✦ Subjects

Image Processing and Computer Vision; Signal, Image and Speech Processing; Computer Imaging, Vision, Pattern Recognition and Graphics