Low-Rank and Sparse Modeling for Visual Analysis

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 pop...

Full description

Bibliographic Details
Other Authors: Fu, Yun (Editor)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2014, 2014
Edition:1st ed. 2014
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
LEADER 02418nmm a2200313 u 4500
001 EB000898945
003 EBX01000000000000000696065
005 00000000000000.0
007 cr|||||||||||||||||||||
008 141103 ||| eng
020 |a 9783319120003 
100 1 |a Fu, Yun  |e [editor] 
245 0 0 |a Low-Rank and Sparse Modeling for Visual Analysis  |h Elektronische Ressource  |c edited by Yun Fu 
250 |a 1st ed. 2014 
260 |a Cham  |b Springer International Publishing  |c 2014, 2014 
300 |a VII, 236 p. 66 illus., 51 illus. in color  |b online resource 
505 0 |a 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 
653 |a Image processing / Digital techniques 
653 |a Computer vision 
653 |a Computer Vision 
653 |a Computer Imaging, Vision, Pattern Recognition and Graphics 
653 |a Signal, Speech and Image Processing 
653 |a Signal processing 
041 0 7 |a eng  |2 ISO 639-2 
989 |b Springer  |a Springer eBooks 2005- 
028 5 0 |a 10.1007/978-3-319-12000-3 
856 4 0 |u https://doi.org/10.1007/978-3-319-12000-3?nosfx=y  |x Verlag  |3 Volltext 
082 0 |a 006.37 
520 |a 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