| Title | : | Large Scale Metric Learning using Locality Sensitive Hashing |
| Speaker | : | Ramamohana Rao Kotagiri (University of Melbourne) |
| Details | : | Tue, 8 Sep, 2015 4:00 PM @ BSB 361 |
| Abstract: | : | Metric learning tries discover mapping of features such that objects belonging a particular class each other in the new space. However, the current methods of discovering such metric mappings are computationally in feasible when the data set is huge with large number of features. My talk will describe the state of the art algorithms for metric learning. I will present our recent work on an efficient approach for discovering metric learning based mappings using Locality Sensitive Hashing (LSH). Our generic approach can accelerate state-of-the-art metric learning while achieving competitive classification accuracy, expanding feasibility by an order of magnitude. Our approach can accelerate Large Margin Nearest Neighbour (LMNN) to learn metrics on 1,000,000 samples in 3.6 minutes which is reduced from 5.8 hours. |
