A matrix factorization framework for jointly analyzing multiple nonnegative data source Gupta, Sunil Kumar, Phung, Dinh, Adams, Brett and Venkatesh, Svetha 2011, A matrix factorization framework for jointly analyzing multiple nonnegative data source, in Proceedings of the 9th Workshop on Text Mining, in conjunction with the 11th SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, [Mesa, Ariz.], pp. 6-15. Attached Files Name Description MIMEType Size Downloads Title A matrix factorization framework for jointly analyzing multiple nonnegative data source Author(s) Gupta, Sunil Kumar orcid.org/0000-0002-3308-1930 Phung, Dinh orcid.org/0000-0002-9977-8247 Adams, Brett Venkatesh, Svetha Conference name Workshop on Text Mining (9th : 2011 : Mesa, Ariz.) Conference location Mesa, Ariz. Conference dates 30 Apr. 2011 Title of proceedings Proceedings of the 9th Workshop on Text Mining, in conjunction with the 11th SIAM International Conference on Data Mining Editor(s) [Unknown] Publication date 2011 Conference series Workshop on Text Mining Start page 6 End page 15 Total pages 10 Publisher Society for Industrial and Applied Mathematics Place of publication [Mesa, Ariz.] Keyword(s) text mining nonnegative matrix factorization arbitrary sharing configurations data mining data sources Summary Nonnegative matrix factorization based methods provide one of the simplest and most effective approaches to text mining. However, their applicability is mainly limited to analyzing a single data source. In this paper, we propose a novel joint matrix factorization framework which can jointly analyze multiple data sources by exploiting their shared and individual structures. The proposed framework is flexible to handle any arbitrary sharing configurations encountered in real world data. We derive an efficient algorithm for learning the factorization and show that its convergence is theoretically guaranteed. We demonstrate the utility and effectiveness of the proposed framework in two real-world applications–improving social media retrieval using auxiliary sources and cross-social media retrieval. Representing each social media source using their textual tags, for both applications, we show that retrieval performance exceeds the existing state-of-the-art techniques. The proposed solution provides a generic framework and can be applicable to a wider context in data mining wherever one needs to exploit mutual and individual knowledge present across multiple data sources. Language eng Field of Research 089999 Information and Computing Sciences not elsewhere classified Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1.1 Full written paper - refereed Persistent URL http://hdl.handle.net/10536/DRO/DU:30044853 Document type: Conference Paper Collection: School of Information Technology   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 01 May 2012, 11:02:32 EST Mon, 07 May 2012, 12:33:07 EST Mon, 07 May 2012, 12:36:50 EST Tue, 08 May 2012, 08:50:51 EST Tue, 08 May 2012, 08:59:08 EST Tue, 08 May 2012, 09:04:53 EST Tue, 28 Jan 2014, 14:42:59 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 337 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Tue, 01 May 2012, 11:02:32 EST

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