NDLI: A Kernel Approach for Semisupervised Metric Learning

Content Provider	IEEE Xplore Digital Library
Author	Dit-Yan Yeung Hong Chang
Copyright Year	1990
Abstract	While distance function learning for supervised learning tasks has a long history, extending it to learning tasks with weaker supervisory information has only been studied recently. In particular, some methods have been proposed for semisupervised metric learning based on pairwise similarity or dissimilarity information. In this paper, we propose a kernel approach for semisupervised metric learning and present in detail two special cases of this kernel approach. The metric learning problem is thus formulated as an optimization problem for kernel learning. An attractive property of the optimization problem is that it is convex and, hence, has no local optima. While a closed-form solution exists for the first special case, the second case is solved using an iterative majorization procedure to estimate the optimal solution asymptotically. Experimental results based on both synthetic and real-world data show that this new kernel approach is promising for nonlinear metric learning
Sponsorship	IEEE Computational Intelligence Society
Page Count	9
File Size	728949
Starting Page	141
Ending Page	149
File Format	PDF
ISSN	10459227
Volume Number	18
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Supervised learning Clustering algorithms Unsupervised learning Semisupervised learning Machine learning algorithms Nearest neighbor searches Principal component analysis History Closed-form solution semisupervised learning Clustering kernel learning metric learning
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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