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15

NIPS
2003

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Learning a Distance Metric from Relative Comparisons

13 years 5 months ago

Learning a Distance Metric from Relative Comparisons

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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.

Matthew Schultz, Thorsten Joachims

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Convex Quadratic Programming | NIPS 2003 | NIPS 2007 | Qualitative Training Data | Support Vector Machine |

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Added	31 Oct 2010
Updated	31 Oct 2010
Type	Conference
Year	2003
Where	NIPS
Authors	Matthew Schultz, Thorsten Joachims

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