Distance metric learning for large margin nearest neighbor classification

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:	177 - 7 self

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Datum	Value	Source
TITLE	Distance metric learning for large margin nearest neighbor classification	INFERENCE
AUTHOR NAME	Kilian Q. Weinberger	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer and Information Science, University of Pennsylvania	SVM HeaderParse 0.2
AUTHOR ADDR	Levine Hall, 3330 Walnut Street, Philadelphia, PA 19104	SVM HeaderParse 0.2
AUTHOR NAME	John Blitzer	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer and Information Science, University of Pennsylvania	SVM HeaderParse 0.2
AUTHOR ADDR	Levine Hall, 3330 Walnut Street, Philadelphia, PA 19104	SVM HeaderParse 0.2
AUTHOR NAME	Lawrence K. Saul	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer and Information Science, University of Pennsylvania	SVM HeaderParse 0.2
AUTHOR ADDR	Levine Hall, 3330 Walnut Street, Philadelphia, PA 19104	SVM HeaderParse 0.2
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	SVM HeaderParse 0.2
YEAR	2006	INFERENCE
VENUE	In NIPS	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	17 found	ParsCit 1.0