Backward-forward least angle shrinkage for sparse quadratic optimization

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dc.contributor.author Zhou, T
dc.contributor.author Tao, D
dc.date.accessioned 2012-02-02T11:07:06Z
dc.date.issued 2010
dc.identifier.citation Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6443 LNCS (PART 1), pp. 388 - 396
dc.identifier.isbn 3642175368
dc.identifier.isbn 9783642175367
dc.identifier.issn 0302-9743
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/16146
dc.description.abstract In compressed sensing and statistical society, dozens of algorithms have been developed to solve ℓ1 penalized least square regression, but constrained sparse quadratic optimization (SQO) is still an open problem. In this paper, we propose backward-forward least angle shrinkage (BF-LAS), which provides a scheme to solve general SQO including sparse eigenvalue minimization. BF-LAS starts from the dense solution, iteratively shrinks unimportant variables' magnitudes to zeros in the backward step for minimizing the ℓ1 norm, decreases important variables' gradients in the forward step for optimizing the objective, and projects the solution on the feasible set defined by the constraints. The importance of a variable is measured by its correlation w.r.t the objective and is updated via least angle shrinkage (LAS). We show promising performance of BF-LAS on sparse dimension reduction. © 2010 Springer-Verlag.
dc.relation.isbasedon 10.1007/978-3-642-17537-4_48
dc.title Backward-forward least angle shrinkage for sparse quadratic optimization
dc.type Conference Proceeding
dc.parent Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.journal.volume PART 1
dc.journal.volume 6443 LNCS
dc.journal.number en_US
dc.publocation Berlin, Germany en_US
dc.identifier.startpage 388 en_US
dc.identifier.endpage 396 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 080108 Neural, Evolutionary and Fuzzy Computation
dc.personcode 111502
dc.percentage 100 en_US
dc.classification.name Neural, Evolutionary and Fuzzy Computation en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom International Conference on Neural Information Processing en_US
dc.date.activity 20101121 en_US
dc.location.activity Sydney, Australia en_US
dc.description.keywords constrained sparse quadratic optimization; backward-forward least angle shrinkage; L1 norm en_US
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10


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