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Best Paper - Information-Theoretic Metric Learning

Published on Feb 4, 202517779 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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Information-theoretic Metric Learning01:40
Mahalanobis distances43:50:59
The Gaussian connection93:23:11
The optimization problem-part01115:25:45
The optimization problem-part02133:50:16
Bergman's method-part01150:34:52
Bergman's method-part02163:20:10
Connection to Kernel learning171:44:28
Kernelization200:33:10
Online metric learning219:59:05
UCI data sets263:16:57
Clarify data sets274:43:24