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	Low rank metric learning via accelerated gradient optimization
	Shen, Yuanyuan ; Yan, Yan ; Wang, Hanzi ; Yan Y(严严) ; Wang HZ(王菡子)
	2014
关键词	Gradient methods Internet Learning algorithms
英文摘要	Conference Name:6th International Conference on Internet Multimedia Computing and Service, ICIMCS 2014. Conference Address: Xiamen, China. Time:July 10, 2014 - July 12, 2014.; National Natural Foundation of China; SIGMM China Chapter; Xiamen University; A basic assumption in many computer vision applications is that the underlying data lie on a low-dimensional subspace. This property has been widely exploited by metric learning algorithms to find a low-dimensional projection of the feature space such that relevant dimensions are high-weighted, while irrelevant dimensions are low-weighted. A sparse or a low rank metric is an effective solution in general. In this paper, we propose to perform metric learning with the low rank constraint. To effectively solve the proposed low rank metric learning problem, we develop an efficient algorithm based on the accelerated gradient method. The proposed algorithm not only learns an effective metric, but also projects data into an intrinsic low-dimensional subspace. Finally, the proposed algorithm is compared with several state-of-the-art metric learning algorithms in the applications of k nearest neighbor classification and face recognition and competitive results are achieved. Copyright 2014 ACM.
语种	英语
出处	http://dx.doi.org/10.1145/2632856.2632932
出版者	Association for Computing Machinery
内容类型	其他
源URL	[http://dspace.xmu.edu.cn/handle/2288/86904]
专题	信息技术－会议论文
推荐引用方式 GB/T 7714	Shen, Yuanyuan,Yan, Yan,Wang, Hanzi,et al. Low rank metric learning via accelerated gradient optimization. 2014-01-01.

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