Convex multi-task feature learning (Englisch)

In: Machine Learning   ;  73 ,  3  ;  243-272  ;  2008

Wie erhalte ich diesen Titel?

Freier Zugriff

Abstract We present a method for learning sparse representations shared across multiple tasks. This method is a generalization of the well-known single-task 1-norm regularization. It is based on a novel non-convex regularizer which controls the number of learned features common across the tasks. We prove that the method is equivalent to solving a convex optimization problem for which there is an iterative algorithm which converges to an optimal solution. The algorithm has a simple interpretation: it alternately performs a supervised and an unsupervised step, where in the former step it learns task-specific functions and in the latter step it learns common-across-tasks sparse representations for these functions. We also provide an extension of the algorithm which learns sparse nonlinear representations using kernels. We report experiments on simulated and real data sets which demonstrate that the proposed method can both improve the performance relative to learning each task independently and lead to a few learned features common across related tasks. Our algorithm can also be used, as a special case, to simply select—not learn—a few common variables across the tasks.

Inhaltsverzeichnis – Band 73, Ausgabe 3

Zeige alle Jahrgänge und Ausgaben

Die Inhaltsverzeichnisse werden automatisch erzeugt und basieren auf den im Index des TIB-Portals verfügbaren Einzelnachweisen der enthaltenen Beiträge. Die Anzeige der Inhaltsverzeichnisse kann daher unvollständig oder lückenhaft sein.

215
Guest editor’s introduction: special issue on inductive transfer learning
Silver, Daniel L. / Bennett, Kristin P. | 2008
221
Flexible latent variable models for multi-task learning
Zhang, Jian / Ghahramani, Zoubin / Yang, Yiming | 2008
243
Convex multi-task feature learning
Argyriou, Andreas / Evgeniou, Theodoros / Pontil, Massimiliano | 2008
273
A notion of task relatedness yielding provable multiple-task learning guarantees
Ben-David, Shai / Borbely, Reba Schuller | 2008
289
Transfer in variable-reward hierarchical reinforcement learning
Mehta, Neville / Natarajan, Sriraam / Tadepalli, Prasad / Fern, Alan | 2008
313
Inductive transfer with context-sensitive neural networks
Silver, Daniel L. / Poirier, Ryan / Currie, Duane | 2008