Soft Clustering Based on Non-negative Matrix Factorization for Longitudinal Data
Author:
Affiliation:
1. The Center for Data Science Education and Research, Shiga University
Publisher
Japanese Society of Applied Statistics
Subject
General Medicine
Link
https://www.jstage.jst.go.jp/article/jappstat/51/1-2/51_1/_pdf
Reference22 articles.
1. Berry, M.W., Gillis, N. and Glineur, F. (2009):Document Classification using Nonnegative Matrix Factorization and Underapproximation,Proceedings of IEEE International Symposium on Circuits and Systems, 2782-2785.
2. Blei, D.M., Ng, A.Y. and Jordan, M. (2003):Latent Dirichlet Allocation,Journal of Machine Learning Research, 3, 993-1022.
3. Deerwester, S.D., Dumais, S., Furnas, G.W., Landauer, T.K. and Harshman, R. (1990):Indexing by latent semantic analysis,Journal of the American Society for Information Science, 41, 391-407.
4. Ding, C., He, X. and Simon, H.D. (2005):On the Equivalence of Nonnegative Matrix Factorization and Spectral Clustering,Proceedings of the 2005 SIAM International Conference on Data Mining,606-610.
5. Gaujoux, R. and Seoighe, C. (2010):A flexible R package for nonnegative matrix factorization,BMC Bioinformatics, 11, 367 (9 pages).
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