Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionComputer Science Program
Computational Bioscience Research Center (CBRC)
Computer Science Program
Structural and Functional Bioinformatics Group
Date
2014-04-17Online Publication Date
2014-04-17Print Publication Date
2015-01Permanent link to this record
http://hdl.handle.net/10754/575597
Metadata
Show full item recordAbstract
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.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/s11042-014-1939-9Sponsors
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.Publisher
Springer Natureae974a485f413a2113503eed53cd6c53
10.1007/s11042-014-1939-9