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dc.contributor.authorYuan, Ganzhao
dc.contributor.authorZhang, Zhenjie
dc.contributor.authorGhanem, Bernard
dc.contributor.authorHao, Zhifeng
dc.date.accessioned2015-08-03T11:02:08Z
dc.date.available2015-08-03T11:02:08Z
dc.date.issued2013-04
dc.identifier.issn09252312
dc.identifier.doi10.1016/j.neucom.2012.10.014
dc.identifier.urihttp://hdl.handle.net/10754/562701
dc.description.abstractLow rank matrix approximation is an attractive model in large scale machine learning problems, because it can not only reduce the memory and runtime complexity, but also provide a natural way to regularize parameters while preserving learning accuracy. In this paper, we address a special class of nonconvex quadratic matrix optimization problems, which require a low rank positive semidefinite solution. Despite their non-convexity, we exploit the structure of these problems to derive an efficient solver that converges to their local optima. Furthermore, we show that the proposed solution is capable of dramatically enhancing the efficiency and scalability of a variety of concrete problems, which are of significant interest to the machine learning community. These problems include the Top-k Eigenvalue problem, Distance learning and Kernel learning. Extensive experiments on UCI benchmarks have shown the effectiveness and efficiency of our proposed method. © 2012.
dc.description.sponsorshipYuan and Hao are supported by NSF-China (61070033, 61100148), NSF-Guangdong (9251009001000005, S2011040004804), Key Technology Research and Development Programs of Guangdong Province (2010B050400011).
dc.publisherElsevier BV
dc.subjectEigenvalue decomposition
dc.subjectKernel learning
dc.subjectLow-rank and sparse matrix approximation
dc.subjectMetric learning
dc.subjectSemidefinite programming
dc.titleLow-rank quadratic semidefinite programming
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentVisual Computing Center (VCC)
dc.contributor.departmentVCC Analytics Research Group
dc.identifier.journalNeurocomputing
dc.contributor.institutionSchool of Computer Science and Engineering, South China University of Technology, China
dc.contributor.institutionAdvanced Digital Sciences Center, Illinois at Singapore Pte, Singapore
dc.contributor.institutionFaculty of Computer, Guangdong University of Technology, China
kaust.personGhanem, Bernard


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