Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGong, D.en
dc.contributor.authorTan, M.en
dc.contributor.authorZhang, Y.en
dc.contributor.authorVan Den Hengel, A.en
dc.contributor.authorShi, Q.en
dc.identifier.citationProceedings of the 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 2017 / pp.1934-1940en
dc.description.abstractUnlike 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.en
dc.description.statementofresponsibilityDong Gong, Mingkui Tan, Yanning Zhang, Anton van den Hengel, Qinfeng Shien
dc.rightsCopyright © 2017, Association for the Advancement of Artificial Intelligence ( All rights reserved.en
dc.titleMPGL: An efficient matching pursuit method for generalized LASSOen
dc.typeConference paperen
dc.contributor.conference31st AAAI Conference on Artificial Intelligence (AAAI-17) (04 Feb 2017 - 09 Feb 2017 : San Francisco)en
pubs.library.collectionAustralian Institute for Machine Learning publicationsen
dc.identifier.orcidVan Den Hengel, A. [0000-0003-3027-8364]en
dc.identifier.orcidShi, Q. [0000-0002-9126-2107]en
Appears in Collections:Australian Institute for Machine Learning publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.