en-de
en-es
en-fr
en-pt
en-sl
en
en-zh
0.25
0.5
0.75
1.25
1.5
1.75
2
Best Paper - Information-Theoretic Metric Learning
Published on Jun 22, 200717773 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
Related categories
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