Sparse structure regularized ranking
dc.contributor.author | Wang, Jim Jing-Yan | |
dc.contributor.author | Sun, Yijun | |
dc.contributor.author | Gao, Xin | |
dc.date.accessioned | 2015-08-24T08:33:46Z | |
dc.date.available | 2015-08-24T08:33:46Z | |
dc.date.issued | 2014-04-17 | |
dc.identifier.citation | Wang, J. J.-Y., Sun, Y., & Gao, X. (2014). Sparse structure regularized ranking. Multimedia Tools and Applications, 74(2), 635–654. doi:10.1007/s11042-014-1939-9 | |
dc.identifier.issn | 13807501 | |
dc.identifier.doi | 10.1007/s11042-014-1939-9 | |
dc.identifier.uri | http://hdl.handle.net/10754/575597 | |
dc.description.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. | |
dc.description.sponsorship | 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. | |
dc.publisher | Springer Nature | |
dc.subject | Multimedia database retrieval | |
dc.subject | Ranking score | |
dc.subject | Sparse representation | |
dc.title | Sparse structure regularized ranking | |
dc.type | Article | |
dc.contributor.department | Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division | |
dc.contributor.department | Computer Science Program | |
dc.contributor.department | Computational Bioscience Research Center (CBRC) | |
dc.contributor.department | Computer Science Program | |
dc.contributor.department | Structural and Functional Bioinformatics Group | |
dc.identifier.journal | Multimedia Tools and Applications | |
dc.contributor.institution | SUNY Buffalo, New York State Ctr Excellence Bioinformat & Life, Buffalo, NY 14203 USA | |
dc.contributor.institution | Soochow Univ, Prov Key Lab Comp Informat Proc Technol, Suzhou 215006, Peoples R China | |
dc.contributor.institution | SUNY Buffalo, Dept Biostat, Dept Comp Sci & Engn, Dept Microbiol & Immunol, Buffalo, NY 14203 USA | |
kaust.person | Gao, Xin | |
dc.date.published-online | 2014-04-17 | |
dc.date.published-print | 2015-01 |
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Computational Bioscience Research Center (CBRC)
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Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
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