Fast solvers and efficient implementations for distance metric learning

Abstract

In this paper we study how to improve nearest neighbor classification by learning a Mahalanobis distance metric. We build on a recently proposed framework for distance metric learning known as large margin nearest neighbor (LMNN) classification. Our paper makes three contributions. First, we describe a highly efficient solver for the particular instance of… (More)
DOI: 10.1145/1390156.1390302

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@inproceedings{Weinberger2008FastSA, title={Fast solvers and efficient implementations for distance metric learning}, author={Kilian Q. Weinberger and Lawrence K. Saul}, booktitle={ICML}, year={2008} }