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Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/2973
Title: Kernel-based metric adaptation with pairwise constraints
Authors: Chang, Hong
Yeung, Dit-Yan
Keywords: Learning algorithms
Distance metric
Semi-supervised metric learning
Kernel matrix adaptation
Issue Date: 2006
Citation: Lecture Notes in Computer Science, v. 3930, 2006, p. 721-730.
Abstract: Many supervised and unsupervised learning algorithms depend on the choice of an appropriate distance metric. While metric learning for supervised learning tasks has a long history, extending it to learning tasks with weaker supervisory information has only been studied very recently. In particular, several methods have been proposed for semi-supervised metric learning based on pairwise (dis)similarity information. In this paper, we propose a kernel-based approach for nonlinear metric learning, which performs locally linear translation in the kernel-induced feature space. We formulate the metric learning problem as a kernel learning problem and solve it efficiently by kernel matrix adaptation. Experimental results based on synthetic and real-world data sets show that our approach is promising for semi-supervised metric learning.
Rights: The original publication is available at http://www.springerlink.com/
URI: http://hdl.handle.net/1783.1/2973
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