Title

Kernel-Based Distance Metric Learning In The Output Space

Abstract

In this paper we present two related, kernelbased Distance Metric Learning (DML) methods. Their respective models non-linearly map data from their original space to an output space, and subsequent distance measurements are performed in the output space via a Mahalanobis metric. The dimensionality of the output space can be directly controlled to facilitate the learning of a low-rank metric. Both methods allow for simultaneous inference of the associated metric and the mapping to the output space, which can be used to visualize the data, when the output space is 2-or 3-dimensional. Experimental results for a collection of classification tasks illustrate the advantages of the proposed methods over other traditional and kernelbased DML approaches. © 2013 IEEE.

Publication Date

12-1-2013

Publication Title

Proceedings of the International Joint Conference on Neural Networks

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/IJCNN.2013.6706862

Socpus ID

84893627690 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84893627690

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