Low-Rank and Sparse Modeling for Visual Analysis / edited by Yun Fu.

Format:

Book

Contributor:

Fu, Yun, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Optical data processing.

Signal processing.

Image processing.

Speech processing systems.

Image Processing and Computer Vision.

Signal, Image and Speech Processing.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Local Subjects:

Image Processing and Computer Vision.

Signal, Image and Speech Processing.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Physical Description:

1 online resource (VII, 236 pages) : 66 illustrations, 51 illustrations in color

Edition:

First edition 2014.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2014.

System Details:

text file PDF

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 Vision, Big Data, and Human-Computer Interaction. This book contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications. · Covers the most state-of-the-art topics of sparse and low-rank modeling · Examines the theory of sparse and low-rank analysis to the real-world practice of sparse and low-rank analysis · Contributions from top experts voicing their unique perspectives included throughout.

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.

Other Format:

Printed edition:

ISBN:

978-3-319-12000-3

9783319120003

Access Restriction:

Restricted for use by site license.