Topic Splitting: A Hierarchical Topic Model Based on Non-Negative Matrix Factorization-Reference-Cited by-同舟云学术

Topic Splitting: A Hierarchical Topic Model Based on Non-Negative Matrix Factorization

Published:2018-07-19 Issue:4 Volume:27 Page:479-496
ISSN:1004-3756
Container-title:Journal of Systems Science and Systems Engineering
language:en
Short-container-title:J. Syst. Sci. Syst. Eng.

Author:

Liu Rui,Wang Xingguang,Wang Deqing,Zuo Yuan,Zhang He,Zheng Xianzhu

Publisher

Springer Science and Business Media LLC

Subject

Information Systems,Control and Systems Engineering

Link

http://link.springer.com/article/10.1007/s11518-018-5375-7/fulltext.html

Reference44 articles.

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3. Choi, S., Algorithms for orthogonal nonnegative matrix factorization. 2008. IEEE International Joint Conference on Neural Networks, 2008: 1828–1832.

4. Chen, Y., Zhang, H., Wu, J., et al. (2015) Modeling emerging, evolving and fading topics using dynamic soft orthogonal NMF with sparse representation. 2015 IEEE International Conference on Data Mining (ICDM). IEEE, 2015: 61–70.

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