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A Scalable Kernel-Based Semisupervised Metric Learning Algorithm with Out-of-Sample Generalization Ability
Yeung, Dit-Yan1; Chang, Hong2; Dai, Guang1
2008-11-01
发表期刊NEURAL COMPUTATION
ISSN0899-7667
卷号20期号:11页码:2839-2861
摘要In recent years, metric learning in the semisupervised setting has aroused a lot of research interest. One type of semisupervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric teaming or unable to scale well with the data set size. In this letter, we propose a nonlinear metric learning method based on the kernel approach.-By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.
收录类别SCI
语种英语
资助项目Council of the Hong Kong Special Administrative Region, China[621706]
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000260113500010
出版者M I T PRESS
引用统计
被引频次:9[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://119.78.100.204/handle/2XEOYT63/11152
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Yeung, Dit-Yan
作者单位1.Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
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GB/T 7714
Yeung, Dit-Yan,Chang, Hong,Dai, Guang. A Scalable Kernel-Based Semisupervised Metric Learning Algorithm with Out-of-Sample Generalization Ability[J]. NEURAL COMPUTATION,2008,20(11):2839-2861.
APA Yeung, Dit-Yan,Chang, Hong,&Dai, Guang.(2008).A Scalable Kernel-Based Semisupervised Metric Learning Algorithm with Out-of-Sample Generalization Ability.NEURAL COMPUTATION,20(11),2839-2861.
MLA Yeung, Dit-Yan,et al."A Scalable Kernel-Based Semisupervised Metric Learning Algorithm with Out-of-Sample Generalization Ability".NEURAL COMPUTATION 20.11(2008):2839-2861.
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