| Date |
8-1-2007 |
| Time |
14:30 |
| Room/Location |
Sala conferenze DISI-3° piano |
| Title |
Multi-task Feature Learning |
| Speaker |
Massimiliano Pontil |
| Affiliation |
University College London - UK |
| Link |
http://www.econ.ucl.ac.uk/index.php
|
| Abstract |
We present a method for learning a low dimensional representation which is shared across a set of multiple related tasks. The method builds upon the well-known 1-norm regularization problem using a new regularizer which controls the number of learned features common for all the tasks. We show that this problem is equivalent to a convex optimization problem and develop an iterative algorithm for solving it. The algorithm has a simple interpretation:
it alternately performs a supervised and an unsupervised step, where in the latter step we learn common-across-tasks representations and in the former
step we learn task-specific functions using these representations. We report experiments on a simulated and a real data set which demonstrate that the proposed method dramatically improves the performance relative to learning each task independently. Our algorithm can also be used, as a special case, to simply select -- not learn -- a few common features across the tasks.
This is joint work with Andreas Argyriou and Theodoros Evgeniou
|
