caa a22 4500 605446792 CHVBK 20210128100129.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s11042-014-1939-9 doi (NATIONALLICENCE)springer-10.1007/s11042-014-1939-9 Sparse structure regularized ranking [Elektronische Daten] [Jim Wang, Yijun Sun, Xin Gao] 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. Springer Science+Business Media New York, 2014 Multimedia database retrieval nationallicence Ranking score nationallicence Sparse representation nationallicence Wang Jim New York State Center of Excellence in Bioinformatics and Life Sciences, University at Buffalo, The State University of New York, 14203, Buffalo, NY, USA aut Sun Yijun New York State Center of Excellence in Bioinformatics and Life Sciences, University at Buffalo, The State University of New York, 14203, Buffalo, NY, USA aut Gao Xin Department of Microbiology and Immunology, Department of Computer Science and Engineering, Department of Biostatistics, University at Buffalo, The State University of New York, 14203, Buffalo, NY, USA aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/2(2015-01-01), 635-654 1380-7501 74:2<635 2015 74 11042 https://doi.org/10.1007/s11042-014-1939-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-014-1939-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wang Jim New York State Center of Excellence in Bioinformatics and Life Sciences, University at Buffalo, The State University of New York, 14203, Buffalo, NY, USA aut NATIONALLICENCE 700 1- Sun Yijun New York State Center of Excellence in Bioinformatics and Life Sciences, University at Buffalo, The State University of New York, 14203, Buffalo, NY, USA aut NATIONALLICENCE 700 1- Gao Xin Department of Microbiology and Immunology, Department of Computer Science and Engineering, Department of Biostatistics, University at Buffalo, The State University of New York, 14203, Buffalo, NY, USA aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/2(2015-01-01), 635-654 1380-7501 74:2<635 2015 74 11042