Paper: Learning Better Data Representation Using Inference-Driven Metric Learning

ACL ID P10-2069
Title Learning Better Data Representation Using Inference-Driven Metric Learning
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2010
Authors

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