Non-negative Matrix Factorization Based Text Mining: Feature Extraction and Classification-Reference-Cited by-同舟云学术

Non-negative Matrix Factorization Based Text Mining: Feature Extraction and Classification

Published:2006 Issue: Volume: Page:703-712
ISSN:0302-9743
Container-title:Neural Information Processing
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Author:

Barman P. C.,Iqbal Nadeem,Lee Soo-Young

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/11893257_78

Reference10 articles.

1. Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)

2. Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing 13. Proc. NIPS 2000, MIT Press, Cambridge (2001)

3. Xu, W., Liu, X., Gong, Y.: Document-Clustering based on Non-Negative Matrix Factorization. In: Proceedings of SIGIR 2003, Toronto, CA, July 28-August 1, 2003, pp. 267–273 (2003)

4. Willett, P.: Document clustering using an inverted file approach. Journal of Information Science 2, 223–231 (1990)

5. Baker, L., McCallum, A.: Distributional clustering of words for text classification. In: Proceedings of ACM SIGIR (1998)

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