On Non-negative Matrix Factorization Using Gaussian Kernels as Covariates
Author:
Affiliation:
1. Faculty of Data Science, Shiga University
Publisher
Japanese Society of Applied Statistics
Link
https://www.jstage.jst.go.jp/article/jappstat/52/2/52_59/_pdf
Reference26 articles.
1. Abe, H. and Yadohisa, H. (2017):A non-negative matrix factorization model based on the zero-inflated Tweedie distribution, Computational Statistics, 32, 475-499.
2. Berry, M.W., Gillis, N., Glineur, F. (2009):Document Classification using Nonnegative Matrix Factorization and Underapproximation,Proceedings of IEEE International Symposium on Circuits and Systems, 2782-2785.
3. Chen, W., Xiya, G. and Binbin, P. (2021):A novel general kernel-based non-negative matrix factorisation approach for face recognition,Connection Science, 34(1), 785-810.
4. Cichocki, A. and Amari, S. (2010):Families of Alpha- Beta- and Gamma- Divergences: Flexible and Robust Measures of Similarities,Entropy, 12(6), 1532-1568.
5. Čopar, A., žitnik, M. and Zupan, B. (2017): Scalable non-negative matrix tri-factorization, BioData Mining, 10(41).
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