@inproceedings{34954fbdea7345e1aa15709288d99e85,
title = "Mini-batch stochastic approaches for accelerated multiplicative updates in nonnegative matrix factorisation with beta-divergence",
abstract = "Nonnegative matrix factorisation (NMF) with β-divergence is a popular method to decompose real world data. In this paper we propose mini-batch stochastic algorithms to perform NMF efficiently on large data matrices. Besides the stochastic aspect, the mini-batch approach allows exploiting intensive computing devices such as general purpose graphical processing units to decrease the processing time and in some cases outperform coordinate descent approach.",
keywords = "GPGPU, Nonnegative matrix factorisation, multiplicative rules, online learning",
author = "Romain Serizel and Slim Essid and Gael Richard",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings ; Conference date: 13-09-2016 Through 16-09-2016",
year = "2016",
month = nov,
day = "8",
doi = "10.1109/MLSP.2016.7738818",
language = "English",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
editor = "Kostas Diamantaras and Aurelio Uncini and Palmieri, \{Francesco A. N.\} and Jan Larsen",
booktitle = "2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings",
}