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Best Paper - Information-Theoretic Metric Learning
Published on Jun 22, 200717774 Views
In this paper, we present an information-theoretic approach to learning a Mahalanobis distance function. We formulate the problem as that of minimizing the differential relative entropy between two mu
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Chapter list
Information-theoretic Metric Learning00:00
Introduction00:21
Our approach01:37
Mahalanobis distances02:37
Problem formulation04:06
The Gaussian connection05:36
The optimization problem-part0106:55
The optimization problem-part0208:01
Bergman's method-part0109:02
Bergman's method-part0209:48
Connection to Kernel learning10:18
Kernelization12:01
Online metric learning13:11
Experimental results14:57
UCI data sets15:47
Clarify data sets16:29
Conclusions17:45