@inproceedings{b6cf0df2a84948a48dd3839aa9962d25,
title = "A novel newton-type algorithm for nonnegative matrix factorization with alpha-divergence",
abstract = "We propose a novel iterative algorithm for nonnegative matrix factorization with the alpha-divergence. The proposed algorithm is based on the coordinate descent and the Newton method. We show that the proposed algorithm has the global convergence property in the sense that the sequence of solutions has at least one convergent subsequence and the limit of any convergent subsequence is a stationary point of the corresponding optimization problem. We also show through numerical experiments that the proposed algorithm is much faster than the multiplicative update rule.",
keywords = "Alpha-divergence, Global convergence, Newton method, Nonnegative matrix factorization",
author = "Satoshi Nakatsu and Norikazu Takahashi",
note = "Funding Information: by JSPS KAKENHI Grant; 24th International Conference on Neural Information Processing, ICONIP 2017 ; Conference date: 14-11-2017 Through 18-11-2017",
year = "2017",
doi = "10.1007/978-3-319-70087-8_36",
language = "English",
isbn = "9783319700861",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "335--344",
editor = "Yuanqing Li and Derong Liu and Shengli Xie and El-Alfy, {El-Sayed M.} and Dongbin Zhao",
booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings",
}