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Distance metric learning for large margin nearest neighbor classification (2006)

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by Kilian Q. Weinberger , John Blitzer , Lawrence K. Saul
Venue:In NIPS
Citations:695 - 14 self
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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   

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