Sparse structure regularized ranking

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• 作者：Jim Jing-Yan Wang (1) (2)
  Yijun Sun (1)
  Xin Gao (3)
  
  1. New York State Center of Excellence in Bioinformatics and Life Sciences ; University at Buffalo ; The State University of New York ; Buffalo ; NY ; 14203 ; USA
  2. Provincial Key Laboratory for Computer Information Processing Technology ; Soochow University ; Suzhou ; 215006 ; China
  3. Department of Microbiology and Immunology ; Department of Computer Science and Engineering ; Department of Biostatistics ; University at Buffalo ; The State University of New York ; Buffalo ; NY ; 14203 ; USA
  
• 关键词：Multimedia database retrieval ; Ranking score ; Sparse representation
• 刊名：Multimedia Tools and Applications
• 出版年：2015
• 出版时间：January 2015
• 年：2015
• 卷：74
• 期：2
• 页码：635-654
• 全文大小：2,132 KB
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• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721

文摘

Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse structure, we assume that each multimedia object could be represented as a sparse linear combination of all other objects, and combination coefficients are regarded as a similarity measure between objects and used to regularize their ranking scores. Moreover, we propose to learn the sparse combination coefficients and the ranking scores simultaneously. A unified objective function is constructed with regard to both the combination coefficients and the ranking scores, and is optimized by an iterative algorithm. Experiments on two multimedia database retrieval data sets demonstrate the significant improvements of the propose algorithm over state-of-the-art ranking score learning algorithms.