DMCA
Multi-task feature learning (2007)
Cached
Download Links
| Venue: | Advances in Neural Information Processing Systems 19 |
| Citations: | 240 - 8 self |
BibTeX
@INPROCEEDINGS{Argyriou07multi-taskfeature,
author = {Andreas Argyriou and Theodoros Evgeniou and Massimiliano Pontil},
title = {Multi-task feature learning},
booktitle = {Advances in Neural Information Processing Systems 19},
year = {2007},
publisher = {MIT Press}
}
Share
 |
 |
 |
 |
||
OpenURL
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 wellknown 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 commonacross-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.
Keyphrases
multi-task feature learning multiple related task special case new regularizer convex optimization problem latter step wellknown 1-norm regularization problem task-specific function learned feature commonacross-tasks representation performance relative low-dimensional representation former step iterative algorithm real data set unsupervised step common feature simple interpretation
