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A matrix factorization framework for jointly analyzing multiple nonnegative data source
conference contribution
posted on 2011-01-01, 00:00 authored by Sunil GuptaSunil Gupta, Quoc-Dinh Phung, B Adams, Svetha VenkateshSvetha VenkateshNonnegative 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.
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Event
Workshop on Text Mining (9th : 2011 : Mesa, Ariz.)Pagination
6 - 15Publisher
Society for Industrial and Applied MathematicsLocation
Mesa, Ariz.Place of publication
[Mesa, Ariz.]Start date
2011-04-30Language
engPublication classification
E1.1 Full written paper - refereedTitle of proceedings
Proceedings of the 9th Workshop on Text Mining, in conjunction with the 11th SIAM International Conference on Data MiningUsage metrics
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