Learning Better Data Representation Using Inference-Driven Metric Learning

Paramveer S. Dhillon, Partha Pratim Talukdar, Koby Crammer

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.