Learning a Distance Metric from Relative Comparisons

This paper presents a method for learning a distance metric from relative 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 methods for SVM training. We empirically evaluate the performance and the modelling flexibility of the algorithm on a collection of text documents.

• Type: Conference
• Year: 2003
• Where: NIPS
• Authors: Matthew Schultz, Thorsten Joachims
• Keywords: Convex Quadratic Programming, NIPS 2003, NIPS 2007, Qualitative Training Data, Support Vector Machine