Semi-supervised metric learning via topology preserving multiple semi-supervised assumptions

doi:10.1016/j.patcog.2013.02.015

科研成果详情

题名	Semi-supervised metric learning via topology preserving multiple semi-supervised assumptions
作者	Wang，Qianying 1; Yuen，Pong C.2,3; Feng，Guocan 1
发表日期	2013
发表期刊	Pattern Recognition
ISSN/eISSN	0031-3203
卷号	46 期号:9 页码:2576-2587
摘要	Learning an appropriate distance metric is a critical problem in pattern recognition. This paper addresses the problem of semi-supervised metric learning. We propose a new regularized semi-supervised metric learning (RSSML) method using local topology and triplet constraints. Our regularizer is designed and developed based on local topology, which is represented by local neighbors from the local smoothness, cluster (low density) and manifold information point of view. The regularizer is then combined with the large margin hinge loss on the triplet constraints. In other words, we keep a large margin between different labeled samples, and in the meanwhile, we use the unlabeled samples to regularize it. Then the semi-supervised metric learning method is developed. We have performed experiments on classification using publicly available databases to evaluate the proposed method. To our best knowledge, this is the only method satisfying all the three semi-supervised assumptions, namely smoothness, cluster (low density) and manifold. Experimental results have shown that the proposed method outperforms state-of-the-art semi-supervised distance metric learning algorithms. © 2013 Elsevier Ltd. All rights reserved.
关键词	Semi-supervised assumptions Semi-supervised metric learning Topology preserving
DOI	10.1016/j.patcog.2013.02.015
URL	查看来源
语种	英语English
Scopus入藏号	2-s2.0-84876733408
引用统计
文献类型	期刊论文
条目标识符	https://repository.uic.edu.cn/handle/39GCC9TT/6538
专题	北师香港浸会大学
通讯作者	Feng，Guocan
作者单位	1.School of Mathematics and Computational Science,Sun Yat-Sen University,Guangdong 510275,China 2.Department of Computer Science,Hong Kong Baptist University,Hong Kong,Hong Kong 3.BNU-HKBU United International College,China
推荐引用方式 GB/T 7714	Wang，Qianying,Yuen，Pong C.,Feng，Guocan. Semi-supervised metric learning via topology preserving multiple semi-supervised assumptions[J]. Pattern Recognition, 2013, 46(9): 2576-2587.
APA	Wang，Qianying, Yuen，Pong C., & Feng，Guocan. (2013). Semi-supervised metric learning via topology preserving multiple semi-supervised assumptions. Pattern Recognition, 46(9), 2576-2587.
MLA	Wang，Qianying,et al."Semi-supervised metric learning via topology preserving multiple semi-supervised assumptions". Pattern Recognition 46.9(2013): 2576-2587.