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Information-Theoretic Metric Learning
Published on 2007-02-256431 Views
We formulate the metric learning problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints on the Mahalanobis distance function. Via a surp
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Presentation
Information-Theoretic Metric Learning00:01
Introduction00:24
Learning a Mahalanobis Distance01:43
Mahalanobis Distance and the Multivariate Gaussian03:12
Problem Formulation05:08
Overview: Optimizing the Model05:44
Low-Rank Kernel Learning06:53
Equivalence to Kernel Learning08:47
Proof Sketch10:15
Optimization via Bregman’s Method11:45
Extensions14:13
Experimental Methodology15:42
Experimental Results16:56
Conclusion18:01