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Sparse subspace clustering via group sparse coding
conference contribution
posted on 2013-01-01, 00:00 authored by Budhaditya Saha, D Pham, Quoc-Dinh Phung, Svetha VenkateshSvetha VenkateshWe propose in this paper a novel sparse subspace clustering method that regularizes sparse subspace representation by exploiting the structural sharing between tasks and data points via group sparse coding. We derive simple, provably convergent, and computationally efficient algorithms for solving the proposed group formulations. We demonstrate the advantage of the framework on three challenging benchmark datasets ranging from medical record data to image and text clustering and show that they consistently outperforms rival methods.
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International Conference on Data Mining (13th : 2013 : Austin, Texas)Pagination
130 - 138Publisher
Society for Industrial and Applied MathematicsLocation
Austin, TexasPlace of publication
Austin, TexasStart date
2013-05-02End date
2013-05-04Language
engPublication classification
E1 Full written paper - refereed; E Conference publicationTitle of proceedings
SDM 2013 : Proceedings of the thirteenth SIAM International Conference on Data MiningUsage metrics
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