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https://hdl.handle.net/2440/116283
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DC Field | Value | Language |
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dc.contributor.author | Gong, D. | - |
dc.contributor.author | Tan, M. | - |
dc.contributor.author | Zhang, Y. | - |
dc.contributor.author | Van Den Hengel, A. | - |
dc.contributor.author | Shi, Q. | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2017, pp.1934-1940 | - |
dc.identifier.issn | 2159-5399 | - |
dc.identifier.issn | 2374-3468 | - |
dc.identifier.uri | http://hdl.handle.net/2440/116283 | - |
dc.description.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. | - |
dc.description.statementofresponsibility | Dong Gong, Mingkui Tan, Yanning Zhang, Anton van den Hengel, Qinfeng Shi | - |
dc.language.iso | en | - |
dc.publisher | AAAI | - |
dc.relation.ispartofseries | AAAI Conference on Artificial Intelligence | - |
dc.rights | Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. | - |
dc.source.uri | https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14429 | - |
dc.title | MPGL: An efficient matching pursuit method for generalized LASSO | - |
dc.type | Conference paper | - |
dc.contributor.conference | 31st AAAI Conference on Artificial Intelligence (AAAI-17) (4 Feb 2017 - 9 Feb 2017 : San Francisco) | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP140102270 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP160100703 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP160103710 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Van Den Hengel, A. [0000-0003-3027-8364] | - |
dc.identifier.orcid | Shi, Q. [0000-0002-9126-2107] | - |
Appears in Collections: | Aurora harvest 8 Australian Institute for Machine Learning publications |
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