Learning a Distance Metric from Relative Comparisons

NeurIPS 2003 pp. 41-48

/neurips/2003/schultz2003neurips-learning/

Abstract

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.

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Text

Schultz and Joachims. "Learning a Distance Metric from Relative Comparisons." Neural Information Processing Systems, 2003.

Markdown

[Schultz and Joachims. "Learning a Distance Metric from Relative Comparisons." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/schultz2003neurips-learning/)

BibTeX

@inproceedings{schultz2003neurips-learning,
  title     = {{Learning a Distance Metric from Relative Comparisons}},
  author    = {Schultz, Matthew and Joachims, Thorsten},
  booktitle = {Neural Information Processing Systems},
  year      = {2003},
  pages     = {41-48},
  url       = {https://mlanthology.org/neurips/2003/schultz2003neurips-learning/}
}