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
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
Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.