UM
Error analysis of stochastic gradient descent ranking
Hong Chen1,2; Yi Tang3; Luoqing Li4; Yuan Yuan3; Xuelong Li3; Yuanyan Tang2
2013-06-01
Source PublicationIEEE Transactions on Cybernetics
ISSN2168-2267
Volume43Issue:3Pages:898-909
Abstract

Ranking is always an important task in machine learning and information retrieval, e.g., collaborative filtering, recommender systems, drug discovery, etc. A kernel-based stochastic gradient descent algorithm with the least squares loss is proposed for ranking in this paper. The implementation of this algorithm is simple, and an expression of the solution is derived via a sampling operator and an integral operator. An explicit convergence rate for leaning a ranking function is given in terms of the suitable choices of the step size and the regularization parameter. The analysis technique used here is capacity independent and is novel in error analysis of ranking learning. Experimental results on real-world data have shown the effectiveness of the proposed algorithm in ranking tasks, which verifies the theoretical analysis in ranking error.

KeywordError Analysis Integral Operator Ranking Reproducing Kernel Hilbert Space Sampling Operator Stochastic Gradient Descent
DOIhttps://doi.org/10.1109/TSMCB.2012.2217957
URLView the original
Indexed BySCIE
Language英语
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000319010000008
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
Fulltext Access
Citation statistics
Cited Times [WOS]:17   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.College of Science, Huazhong Agricultural University, Wuhan 430070, China
2.Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau
3.Center for Optical Imagery Analysis and Learning, State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China
4.Key Laboratory of Applied Mathematics of Hubei Province and the Faculty of Mathematics and Computer Science, Hubei University, Wuhan 430062, China.
First Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Hong Chen,Yi Tang,Luoqing Li,et al. Error analysis of stochastic gradient descent ranking[J]. IEEE Transactions on Cybernetics,2013,43(3):898-909.
APA Hong Chen,Yi Tang,Luoqing Li,Yuan Yuan,Xuelong Li,&Yuanyan Tang.(2013).Error analysis of stochastic gradient descent ranking.IEEE Transactions on Cybernetics,43(3),898-909.
MLA Hong Chen,et al."Error analysis of stochastic gradient descent ranking".IEEE Transactions on Cybernetics 43.3(2013):898-909.
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