When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores

Handle URI:
http://hdl.handle.net/10754/625649
Title:
When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores
Authors:
Wang, Jim Jing-Yan; Cui, Xuefeng; Yu, Ge; Guo, Lili; Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays an important role. Up to now, these two problems have always been considered separately, assuming that data coding and ranking are two independent and irrelevant problems. However, is there any internal relationship between sparse coding and ranking score learning? If yes, how to explore and make use of this internal relationship? In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm. To explore the local distribution in the sparse code space, and also to bridge coding and ranking problems, we assume that in the neighborhood of each data point, the ranking scores can be approximated from the corresponding sparse codes by a local linear function. By considering the local approximation error of ranking scores, the reconstruction error and sparsity of sparse coding, and the query information provided by the user, we construct a unified objective function for learning of sparse codes, the dictionary and ranking scores. We further develop an iterative algorithm to solve this optimization problem.
KAUST Department:
Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Wang JJ-Y, Cui X, Yu G, Guo L, Gao X (2017) When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores. Neural Computing and Applications. Available: http://dx.doi.org/10.1007/s00521-017-3102-9.
Publisher:
Springer Nature
Journal:
Neural Computing and Applications
Issue Date:
28-Jun-2017
DOI:
10.1007/s00521-017-3102-9
Type:
Article
ISSN:
0941-0643; 1433-3058
Sponsors:
The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST) and the National Natural Science Foundation of China under the Grant No. 61502463.
Additional Links:
http://link.springer.com/article/10.1007/s00521-017-3102-9
Appears in Collections:
Articles; 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.authorCui, Xuefengen
dc.contributor.authorYu, Geen
dc.contributor.authorGuo, Lilien
dc.contributor.authorGao, Xinen
dc.date.accessioned2017-10-03T12:49:31Z-
dc.date.available2017-10-03T12:49:31Z-
dc.date.issued2017-06-28en
dc.identifier.citationWang JJ-Y, Cui X, Yu G, Guo L, Gao X (2017) When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores. Neural Computing and Applications. Available: http://dx.doi.org/10.1007/s00521-017-3102-9.en
dc.identifier.issn0941-0643en
dc.identifier.issn1433-3058en
dc.identifier.doi10.1007/s00521-017-3102-9en
dc.identifier.urihttp://hdl.handle.net/10754/625649-
dc.description.abstractSparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays an important role. Up to now, these two problems have always been considered separately, assuming that data coding and ranking are two independent and irrelevant problems. However, is there any internal relationship between sparse coding and ranking score learning? If yes, how to explore and make use of this internal relationship? In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm. To explore the local distribution in the sparse code space, and also to bridge coding and ranking problems, we assume that in the neighborhood of each data point, the ranking scores can be approximated from the corresponding sparse codes by a local linear function. By considering the local approximation error of ranking scores, the reconstruction error and sparsity of sparse coding, and the query information provided by the user, we construct a unified objective function for learning of sparse codes, the dictionary and ranking scores. We further develop an iterative algorithm to solve this optimization problem.en
dc.description.sponsorshipThe research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST) and the National Natural Science Foundation of China under the Grant No. 61502463.en
dc.publisherSpringer Natureen
dc.relation.urlhttp://link.springer.com/article/10.1007/s00521-017-3102-9en
dc.subjectDatabase retrievalen
dc.subjectData representationen
dc.subjectSparse codingen
dc.subjectLearning to ranken
dc.subjectNearest neighborsen
dc.titleWhen sparse coding meets ranking: a joint framework for learning sparse codes and ranking scoresen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalNeural Computing and Applicationsen
dc.contributor.institutionKey Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, Chinaen
kaust.authorWang, Jim Jing-Yanen
kaust.authorCui, Xuefengen
kaust.authorGao, Xinen
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