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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Chang, H., & Yeung, D. Y. (2006). Kernel-based metric adaptation with pairwise constraints. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3930 LNAI, pp. 721–730). Springer Verlag. https://doi.org/10.1007/11739685_75
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