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DTSTART;TZID=UTC:20111117T150000
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DTSTAMP:20260408T034841
CREATED:20170124T102153Z
LAST-MODIFIED:20170124T102153Z
UID:625-1321542000-1321542000@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: LAROS: a convex optimization approach for feature extraction
DESCRIPTION:Title: LAROS: a convex optimization approach for feature extractionSpeaker: Dr. Xuan Vinh Doan Affiliation: Warwick Business School – University of WarwickLocation: Room 572A Huxley BuildingTime: 3:00pm \nAbstract. We propose a convex optimization formulation using nuclear norm and $ell_1$-norm to find a large approximately rank-one submatrix (LAROS) for a given matrix. We are able to characterize the low-rank and sparsity structure of the resulting solutions. We show that our model can recover low-rank submatrices for matrices with subgaussian random noises. We solve the proposed model using a proximal point algorithm and show that it can be applied to applications in feature extraction. This is joint work with Stephen Vavasis and Kim-Chuan Toh. \nAbout the speaker.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-laros-a-convex-optimization-approach-for-feature-extraction/
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