Ground metric learning
Volume: 15, Issue: 1, Pages: 533 - 564
Published: Jan 1, 2014
Abstract
Optimal transport distances have been used for more than a decade in machine learning to compare histograms of features. They have one parameter: the ground metric, which can be any metric between the features themselves. As is the case for all parameterized distances, optimal transport distances can only prove useful in practice when this parameter is carefully chosen. To date, the only option available to practitioners to set the ground metric...
Paper Details
Title
Ground metric learning
Published Date
Jan 1, 2014
Volume
15
Issue
1
Pages
533 - 564
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