Please use this identifier to cite or link to this item: http://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 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 2017 / pp.1934-1940
Publisher: AAAI
Issue Date: 2017
Conference Name: 31st AAAI Conference on Artificial Intelligence (AAAI-17) (04 Feb 2017 - 09 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.
RMID: 0030077250
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:Australian Institute for Machine Learning publications

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