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Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/6579
Title: A scalable kernel-based semisupervised metric learning algorithm with out-of-sample generalization ability
Authors: Yeung, Dit-Yan
Chang, Hong
Dai, Guang
Keywords: Metric learning
Kernel matrix
Semisupervised learning
Issue Date: Nov-2008
Citation: Neural computation, v. 20, no. 11, 2008, p. 2839-2861
Abstract: In recent years, metric learning in the semisupervised setting has arouseda 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 learning 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.
Rights: We would like to give credit to MIT for granting us permission to repost this article.
URI: http://hdl.handle.net/1783.1/6579
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