Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/116283
Type: | Conference paper |
Title: | MPGL: An efficient matching pursuit method for generalized LASSO |
Author: | Gong, D. Tan, M. Zhang, Y. Van Den Hengel, A. Shi, Q. |
Citation: | Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2017, pp.1934-1940 |
Publisher: | AAAI |
Issue Date: | 2017 |
Series/Report no.: | AAAI Conference on Artificial Intelligence |
ISSN: | 2159-5399 2374-3468 |
Conference Name: | 31st AAAI Conference on Artificial Intelligence (AAAI-17) (4 Feb 2017 - 9 Feb 2017 : San Francisco) |
Statement of Responsibility: | Dong Gong, Mingkui Tan, Yanning Zhang, Anton van den Hengel, Qinfeng Shi |
Abstract: | Unlike traditional LASSO enforcing sparsity on the variables, Generalized LASSO (GL) enforces sparsity on a linear transformation of the variables, gaining flexibility and success in many applications. However, many existing GL algorithms do not scale up to high-dimensional problems, and/or only work well for a specific choice of the transformation. We propose an efficient Matching Pursuit Generalized LASSO (MPGL) method, which overcomes these issues, and is guaranteed to converge to a global optimum. We formulate the GL problem as a convex quadratic constrained linear programming (QCLP) problem and tailor-make a cutting plane method. More specifically, our MPGL iteratively activates a subset of nonzero elements of the transformed variables, and solves a subproblem involving only the activated elements thus gaining significant speed-up. Moreover, MPGL is less sensitive to the choice of the trade-off hyper-parameter between data fitting and regularization, and mitigates the long-standing hyper-parameter tuning issue in many existing methods. Experiments demonstrate the superior efficiency and accuracy of the proposed method over the state-of-the-arts in both classification and image processing tasks. |
Rights: | Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. |
Grant ID: | http://purl.org/au-research/grants/arc/DP140102270 http://purl.org/au-research/grants/arc/DP160100703 http://purl.org/au-research/grants/arc/DP160103710 |
Published version: | https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14429 |
Appears in Collections: | Aurora harvest 8 Australian Institute for Machine Learning publications |
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