Sparse structure regularized ranking

Handle URI:
http://hdl.handle.net/10754/575597
Title:
Sparse structure regularized ranking
Authors:
Wang, Jim Jing-Yan; Sun, Yijun; Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer Science Program; Structural and Functional Bioinformatics Group
Publisher:
Springer Nature
Journal:
Multimedia Tools and Applications
Issue Date:
17-Apr-2014
DOI:
10.1007/s11042-014-1939-9
Type:
Article
ISSN:
13807501
Sponsors:
Jim Jing-Yan Wang and Yijun Sun are in part supported by US National Science Foundation under grant No. DBI-1062362. The study is supported by grants from Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, China, and King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
Appears in Collections:
Articles; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorWang, Jim Jing-Yanen
dc.contributor.authorSun, Yijunen
dc.contributor.authorGao, Xinen
dc.date.accessioned2015-08-24T08:33:46Zen
dc.date.available2015-08-24T08:33:46Zen
dc.date.issued2014-04-17en
dc.identifier.issn13807501en
dc.identifier.doi10.1007/s11042-014-1939-9en
dc.identifier.urihttp://hdl.handle.net/10754/575597en
dc.description.abstractLearning 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.en
dc.description.sponsorshipJim Jing-Yan Wang and Yijun Sun are in part supported by US National Science Foundation under grant No. DBI-1062362. The study is supported by grants from Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, China, and King Abdullah University of Science and Technology (KAUST), Saudi Arabia.en
dc.publisherSpringer Natureen
dc.subjectMultimedia database retrievalen
dc.subjectRanking scoreen
dc.subjectSparse representationen
dc.titleSparse structure regularized rankingen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentStructural and Functional Bioinformatics Groupen
dc.identifier.journalMultimedia Tools and Applicationsen
dc.contributor.institutionSUNY Buffalo, New York State Ctr Excellence Bioinformat & Life, Buffalo, NY 14203 USAen
dc.contributor.institutionSoochow Univ, Prov Key Lab Comp Informat Proc Technol, Suzhou 215006, Peoples R Chinaen
dc.contributor.institutionSUNY Buffalo, Dept Biostat, Dept Comp Sci & Engn, Dept Microbiol & Immunol, Buffalo, NY 14203 USAen
kaust.authorGao, Xinen
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