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Sparse subspace clustering via group sparse coding Saha, Budhaditya, Pham, Duc Son, Phung, Dinh and Venkatesh, Svetha 2013, Sparse subspace clustering via group sparse coding, in SDM 2013 : Proceedings of the thirteenth SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, Austin, Texas, pp. 130-138. Attached Files Name Description MIMEType Size Downloads Title Sparse subspace clustering via group sparse coding Author(s) Saha, Budhaditya Pham, Duc Son Phung, Dinh Venkatesh, Svetha Conference name International Conference on Data Mining (13th : 2013 : Austin, Texas) Conference location Austin, Texas Conference dates 2-4 May 2013 Title of proceedings SDM 2013 : Proceedings of the thirteenth SIAM International Conference on Data Mining Editor(s) [Unknown] Publication date 2013 Conference series International Conference on Data Mining Start page 130 End page 138 Total pages 9 Publisher Society for Industrial and Applied Mathematics Place of publication Austin, Texas Summary We 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. Language eng Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1 Full written paper - refereed Persistent URL http://hdl.handle.net/10536/DRO/DU:30053589 Document type: Conference Paper Collection: Centre for Pattern Recognition and Data Analytics Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 12 Jul 2013, 15:22:36 EST Fri, 12 Jul 2013, 15:56:47 EST Mon, 15 Jul 2013, 14:28:00 EST Tue, 16 Jul 2013, 16:56:41 EST Wed, 17 Jul 2013, 09:00:16 EST Thu, 20 Feb 2014, 07:38:58 EST Filtered Full Access Statistics: 63 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Fri, 12 Jul 2013, 15:22:36 EST

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