Learning Better Data Representation Using Inference-Driven Metric Learning

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

• Type: Journal
• Year: 2010
• Where: ACL
• Authors: Paramveer S. Dhillon, Partha Pratim Talukdar, Koby Crammer
• Keywords: ACL 2010, Computational Linguistics, Metric Learning Algorithm, Semisupervised Metric Learning, Unsupervised Dimensionality Reduction