Sparse subspace representation for spectral document clustering Saha, Budhaditya, Phung, Dinh, Pham, Duc Son and Venkatesh, Svetha 2012, Sparse subspace representation for spectral document clustering, in ICDM 2012 : Proceedings of the 12th IEEE International Conference on Data Mining, IEEE, Piscataway, N.J., pp. 1092-1097, doi: 10.1109/ICDM.2012.46. Attached Files Name Description MIMEType Size Downloads Title Sparse subspace representation for spectral document clustering Author(s) Saha, Budhaditya orcid.org/0000-0001-8011-6801 Phung, Dinh orcid.org/0000-0002-9977-8247 Pham, Duc Son Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Conference name Data Mining. Conference (12th : 2012 : Brussels, Belgium) Conference location Brussels, Belgium Conference dates 10 -13 Dec. 2012 Title of proceedings ICDM 2012 : Proceedings of the 12th IEEE International Conference on Data Mining Editor(s) Zaki, Mohammed J. Siebes, Arno Xu, Yu Jeffrey Goethals, Bart Wu, Xindong Publication date 2012 Conference series Data Mining Conference Start page 1092 End page 1097 Total pages 6 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) document clustering sparse representation Summary We present a novel method for document clustering using sparse representation of documents in conjunction with spectral clustering. An ℓ1-norm optimization formulation is posed to learn the sparse representation of each document, allowing us to characterize the affinity between documents by considering the overall information instead of traditional pair wise similarities. This document affinity is encoded through a graph on which spectral clustering is performed. The decomposition into multiple subspaces allows documents to be part of a sub-group that shares a smaller set of similar vocabulary, thus allowing for cleaner clusters. Extensive experimental evaluations on two real-world datasets from Reuters-21578 and 20Newsgroup corpora show that our proposed method consistently outperforms state-of-the-art algorithms. Significantly, the performance improvement over other methods is prominent for this datasets. ISBN 9780769549057 Language eng DOI 10.1109/ICDM.2012.46 Field of Research 109999 Technology not elsewhere classified Socio Economic Objective 970110 Expanding Knowledge in Technology HERDC Research category E1 Full written paper - refereed Persistent URL http://hdl.handle.net/10536/DRO/DU:30051784 Document type: Conference Paper Collections: Centre for Pattern Recognition and Data Analytics 2018 ERA Submission 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 Thu, 04 Apr 2013, 12:59:55 EST Thu, 04 Apr 2013, 13:26:20 EST Thu, 04 Apr 2013, 13:26:57 EST Tue, 07 May 2013, 17:29:43 EST Wed, 08 May 2013, 08:36:08 EST Fri, 24 May 2013, 13:36:36 EST Mon, 27 May 2013, 08:41:24 EST Mon, 27 May 2013, 13:23:57 EST Wed, 21 Oct 2015, 14:44:39 EST Tue, 31 May 2016, 12:24:28 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 344 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Thu, 04 Apr 2013, 12:59:55 EST

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