Low-rank and sparse modeling for visual analysis [electronic resource] / Yun Fu, editor.

Low-rank and sparse modeling for visual analysis [electronic resource] / Yun Fu, editor.

9783319120003 electronic book
331912000X electronic book
9783319119991
3319119990

Cham : Springer, 2014.

English

1 online resource (vii, 236 pages) : illustrations (some color)

10.1007/978-3-319-12000-3 doi

TA1650 .L69 2014eb

006.4/2

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

Includes index.

Access limited to authorized users.

Online resource; title from PDF title page (SpringerLink, viewed November 19, 2014).

Print version: 9783319119991

Online Resources > Ebooks
All Resources