Title

Reduced-Rank Local Distance Metric Learning

Keywords

Local Metric; Majorization Minimization; Metric Learning; Proximal Subgradient Descent

Abstract

We propose a new method for local metric learning based on a conical combination of Mahalanobis metrics and pair-wise similarities between the data. Its formulation allows for controlling the rank of the metrics' weight matrices. We also offer a convergent algorithm for training the associated model. Experimental results on a collection of classification problems imply that the new method may offer notable performance advantages over alternative metric learning approaches that have recently appeared in the literature. © 2013 Springer-Verlag.

Publication Date

10-31-2013

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

8190 LNAI

Issue

PART 3

Number of Pages

224-239

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-642-40994-3_15

Socpus ID

84886567508 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84886567508

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