Low-Rank and Sparse Modeling for Visual Analysis

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...

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Bibliographic Details
Published in:Springer eBooks
Corporate Author: SpringerLink (Online service)
Other Authors: Fu, Yun (Editor)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2014.
Subjects:
Computer Science.
Computer vision.
Image Processing and Computer Vision.
Signal, Image and Speech Processing.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Online Access:http://dx.doi.org/10.1007/978-3-319-12000-3
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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.

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