TY  - BOOK
AU  - Fu,Yun
ED  - SpringerLink (Online service)
TI  - Low-Rank and Sparse Modeling for Visual Analysis
SN  - 9783319120003
AV  - TA1637-1638 
U1  - 006.6 23
PY  - 2014///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Computer Science
KW  - Computer vision
KW  - Image Processing and Computer Vision
KW  - Signal, Image and Speech Processing
KW  - Computer Imaging, Vision, Pattern Recognition and Graphics
N1  - 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
N2  - 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
UR  - http://dx.doi.org/10.1007/978-3-319-12000-3
ER  -