Seminar in Computing + Mathematical Sciences

Wednesday January 25, 2017 4:00 PM

A tutorial on metric learning with some recent advances

Speaker: Nakul Verma, Janelia Research Campus HHMI
Location: Annenberg 213
Goal of metric learning is to learn a notion of distance---or a metric---in the representation space that yields good prediction performance on data. In this tutorial we explore some classic ways one can efficiently find good metrics. Starting from the basics, we'll cover classic techniques like Mahalanobis Metric for Clustering (MMC) and Large Margin Nearest Neighbor (LMNN) and discuss key principles that make these techniques effective in improving prediction performance. We will also study some extensions and see how metric learning has helped in ranking problems (information retrieval) and large scale classification. 
 
Series Special Seminars in Computing + Mathematical Sciences

Contact: Sheila shull at 626.395.4560 sheila@caltech.edu