Learning a Distance Metric from Relative Comparisons

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

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Matthew Schultz, Thorsten Joachims


This paper presents a method for learning a distance metric from rel- ative comparison such as “A is closer to B than A is to C”. Taking a Support Vector Machine (SVM) approach, we develop an algorithm that provides a flexible way of describing qualitative training data as a set of constraints. We show that such constraints lead to a convex quadratic programming problem that can be solved by adapting standard meth- ods for SVM training. We empirically evaluate the performance and the modelling flexibility of the algorithm on a collection of text documents.