HKUST Institutional Repository >
Computer Science and Engineering >
CSE Conference Papers >
Please use this identifier to cite or link to this item:
|Title: ||Accelerated gradient method for multi-task sparse learning problem|
|Authors: ||Chen, Xi|
Kwok, James Tin-Yau
Carbonell, J. G.
|Keywords: ||L-1-infinity regularization|
|Issue Date: ||Dec-2009 |
|Citation: ||Proceedings Ninth IEEE International Conference on Data Mining (ICDM '09), 6-9 December 2009, Miami, FL, USA, p. 746-751|
|Abstract: ||Many real world learning problems can be recast as multi-task learning problems which utilize correlations among different tasks to obtain better generalization performance than learning each task individually. The feature selection problem in multi-task setting has many applications in fields of computer vision, text classification and bio-informatics. Generally, it can be realized by solving a L-1-infinity regularized optimization problem. And the solution automatically yields the joint sparsity among different tasks. However, due to the nonsmooth nature of the L-1-infinity norm, there lacks an efficient training algorithm for solving such problem with general convex loss functions. In this paper, we propose an accelerated gradient method based on an 'optimal' first order black-box method named after Nesterov and provide the convergence rate for smooth convex loss functions. For nonsmooth convex loss functions, such as hinge loss, our method still has fast convergence rate empirically. Moreover, by exploiting the structure of the L-1-infinity ball, we solve the black-box oracle in Nesterov's method by a simple sorting scheme. Our method is suitable for large-scale multi-task learning problem since it only utilizes the first order information and is very easy to implement. Experimental results show that our method significantly outperforms the most state-of-the-art methods in both convergence speed and learning accuracy.|
|Rights: ||© 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.|
|Appears in Collections:||CSE Conference Papers|
Files in This Item:
All items in this Repository are protected by copyright, with all rights reserved.