Learning better data representation using inference-driven metric learning

Authors:
Paramveer S. Dhillon;Partha Pratim Talukdar;Koby Crammer
Affiliations:
Univ. of Penn., Philadelphia, PA;Microsoft Research, Mountain View, CA;The Technion, Haifa, Israel
Venue:
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Year:
2010

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Abstract

We initiate a study comparing effectiveness of the transformed spaces learned by recently proposed supervised, and semi-supervised metric learning algorithms to those generated by previously proposed unsupervised dimensionality reduction methods (e.g., PCA). Through a variety of experiments on different real-world datasets, we find IDML-IT, a semi-supervised metric learning algorithm to be the most effective.