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Low-Rank Modeling and Its Applications in Image Analysis

Published: 19 December 2014 Publication History

Abstract

Low-rank modeling generally refers to a class of methods that solves problems by representing variables of interest as low-rank matrices. It has achieved great success in various fields including computer vision, data mining, signal processing, and bioinformatics. Recently, much progress has been made in theories, algorithms, and applications of low-rank modeling, such as exact low-rank matrix recovery via convex programming and matrix completion applied to collaborative filtering. These advances have brought more and more attention to this topic. In this article, we review the recent advances of low-rank modeling, the state-of-the-art algorithms, and the related applications in image analysis. We first give an overview of the concept of low-rank modeling and the challenging problems in this area. Then, we summarize the models and algorithms for low-rank matrix recovery and illustrate their advantages and limitations with numerical experiments. Next, we introduce a few applications of low-rank modeling in the context of image analysis. Finally, we conclude this article with some discussions.

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  1. Low-Rank Modeling and Its Applications in Image Analysis

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    cover image ACM Computing Surveys
    ACM Computing Surveys  Volume 47, Issue 2
    January 2015
    827 pages
    ISSN:0360-0300
    EISSN:1557-7341
    DOI:10.1145/2658850
    • Editor:
    • Sartaj Sahni
    Issue’s Table of Contents
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    Publication History

    Published: 19 December 2014
    Accepted: 01 September 2014
    Revised: 01 September 2014
    Received: 01 January 2014
    Published in CSUR Volume 47, Issue 2

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    1. Low-rank modeling
    2. image analysis
    3. matrix factorization
    4. optimization

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