Low-Rank and Sparse Modeling for Visual Analysis

by Yun Fu (Editor)

Overview

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.

Product Details

ISBN-13:	9783319120003
Publisher:	Springer-Verlag New York, LLC
Publication date:	10/30/2014
Sold by:	Barnes & Noble
Format:	eBook
Pages:	236
File size:	7 MB

About the Author

Yun Fu is an Assistant Professor, ECE and CS, Northeastern University

Table of 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.