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Distance metric learning for large margin nearest neighbor classification (2006)
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| Venue: | In NIPS |
| Citations: | 677 - 14 self |
BibTeX
@INPROCEEDINGS{Weinberger06distancemetric,
author = {Kilian Q. Weinberger and John Blitzer and Lawrence K. Saul},
title = {Distance metric learning for large margin nearest neighbor classification},
booktitle = {In NIPS},
year = {2006},
publisher = {MIT Press}
}
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Abstract
We show how to learn a Mahanalobis distance metric for k-nearest neighbor (kNN) classification by semidefinite programming. The metric is trained with the goal that the k-nearest neighbors always belong to the same class while examples from different classes are separated by a large margin. On seven data sets of varying size and difficulty, we find that metrics trained in this way lead to significant improvements in kNN classification—for example, achieving a test error rate of 1.3 % on the MNIST handwritten digits. As in support vector machines (SVMs), the learning problem reduces to a convex optimization based on the hinge loss. Unlike learning in SVMs, however, our framework requires no modification or extension for problems in multiway (as opposed to binary) classification. 1
Keyphrases
large margin neighbor classification distance metric learning k-nearest neighbor significant improvement support vector machine mahanalobis distance way lead different class convex optimization hinge loss data set knn classification test error rate learning problem mnist handwritten digit semidefinite programming
