Learning Bregman Distance Functions for Structural Learning to Rank

doi:10.1109/TKDE.2017.2654250

OPT OpenIR > 光谱成像技术研究室

	Learning Bregman Distance Functions for Structural Learning to Rank
	Li, Xi 1,2; Pi, Te 3; Zhang, Zhongfei 3; Zhao, Xueyi 3; Wang, Meng 4; Li, Xuelong 5; Yu, Philip S.6; Li, X
作者部门	光学影像学习与分析中心
	2017-09-01
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	29 期号:9 页码:1916-1927
产权排序	5
摘要	We study content-based learning to rank from the perspective of learning distance functions. Standardly, the two key issues of learning to rank, feature mappings and score functions, are usually modeled separately, and the learning is usually restricted to modeling a linear distance function such as the Mahalanobis distance. However, the modeling of feature mappings and score functions are mutually interacted, and the patterns underlying the data are probably complicated and nonlinear. Thus, as a general nonlinear distance family, the Bregman distance is a suitable distance function for learning to rank, due to its strong generalization ability for distance functions, and its nonlinearity for exploring the general patterns of data distributions. In this paper, we study learning to rank as a structural learning problem, and devise a Bregman distance function to build the ranking model based on structural SVM. To improve the model robustness to outliers, we develop a robust structural learning framework for the ranking model. The proposed model Robust Structural Bregman distance functions Learning to Rank (RSBLR) is a general and unified framework for learning distance functions to rank. The experiments of data ranking on real-world datasets show the superiority of this method to the state-of-the-art literature, as well as its robustness to the noisily labeled outliers.
文章类型	Article
关键词	Learning To Rank Bregman Distance Structural Svm Robust Structural Learning
学科领域	Computer Science, Artificial Intelligence
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TKDE.2017.2654250
收录类别	SCI
关键词[WOS]	OUTLIER REMOVAL
语种	英语
WOS研究方向	Computer Science ; Engineering
项目资助者	National Natural Science Foundation of China(U1509206 ; National Basic Research Program of China(2012CB316400 ; Alibaba-Zhejiang University Joint Institute of Frontier Technologies ; 61472353 ; 2015CB352302) ; 61672456)
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000407433900011
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/29218
专题	光谱成像技术研究室
通讯作者	Li, X
作者单位	1.Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Zhejiang, Peoples R China 2.Alibaba Zhejiang Univ, Joint Inst Frontier Technol, Hangzhou, Zhejiang, Peoples R China 3.Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou 310027, Zhejiang, Peoples R China 4.Hefei Univ Technol, Sch Comp & Informat, Hefei 230009, Anhui, Peoples R China 5.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China 6.Univ Illinois, Dept Comp Sci, Chicago, IL 60607 USA
推荐引用方式 GB/T 7714	Li, Xi,Pi, Te,Zhang, Zhongfei,et al. Learning Bregman Distance Functions for Structural Learning to Rank[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2017,29(9):1916-1927.
APA	Li, Xi.,Pi, Te.,Zhang, Zhongfei.,Zhao, Xueyi.,Wang, Meng.,...&Li, X.(2017).Learning Bregman Distance Functions for Structural Learning to Rank.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,29(9),1916-1927.
MLA	Li, Xi,et al."Learning Bregman Distance Functions for Structural Learning to Rank".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 29.9(2017):1916-1927.

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