Backward-Forward Least Angle Shrinkage for Sparse Quadratic Optimization

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Show simple item record Zhou, Tianyi en_US Tao, Dacheng en_US
dc.contributor.editor Wong, Kevin K.W.; Mendis, B. Sumudu U.; Bouzerdoum, Abdesselam en_US 2012-02-02T11:07:06Z 2012-02-02T11:07:06Z 2010 en_US
dc.identifier 2010001749 en_US
dc.identifier.citation Zhou Tianyi and Tao Dacheng 2010, 'Backward-Forward Least Angle Shrinkage for Sparse Quadratic Optimization', , Springer, Berlin, Germany, , pp. 388-396. en_US
dc.identifier.issn 978-3-642-17536-7 en_US
dc.identifier.other E1 en_US
dc.description.abstract In compressed sensing and statistical society, dozens of algorithms have been developed to solve a??1 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 variablesa?? magnitudes to zeros in the backward step for minimizing the a??1 norm, decreases important variablesa?? 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. en_US
dc.language English en_US
dc.publisher Springer en_US
dc.relation.isbasedon en_US
dc.title Backward-Forward Least Angle Shrinkage for Sparse Quadratic Optimization en_US
dc.parent Proceedings, Part I of the 17th International Conference on Neural Information Processing: Theory and Algorithms (ICONIP 2010) en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Berlin, Germany en_US
dc.identifier.startpage 388 en_US
dc.identifier.endpage 396 en_US FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 080108 en_US
dc.personcode 11201340 en_US
dc.personcode 111502 en_US
dc.percentage 100 en_US 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 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
dc.staffid en_US
dc.staffid 111502 en_US

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