Theano based GPGPU implementation of NMF with beta-diveregence and mini-batch multplicative updates.
beta_nmf_minibatch need Python >= 2.7, numpy >= 10.1, Theano >= 0.8, scikit-learn >= 0.17.1, h5py >= 2.5, itertools and more_itertools
Documentation available at http://rserizel.github.io/minibatchNMF/
A short example is available as a notebook
If you are using this source code please consider citing the following paper:
R. Serizel, S. Essid, and G. Richard. “Mini-batch stochastic approaches for accelerated multiplicative updates in nonnegative matrix factorisation with beta-divergence”. Accepted for publication In Proc. of MLSP, p. 5, 2016.
Bibtex
@inproceedings{serizel2016batch,
title={Mini-batch stochastic approaches for accelerated multiplicative updates in nonnegative matrix factorisation with beta-divergence},
author={Serizel, Romain and Essid, Slim and Richard, Ga{\"e}l},
booktitle={IEEE International Workshop on Machine Learning for Signal Processing (MLSP)},
pages={5470--5474},
year={2016},
organization={IEEE}
}
Romain Serizel, 2014 -- Present
Copyright 2014-2016 Romain Serizel
This software is distributed under the terms of the GNU Public License version 3 (http://www.gnu.org/licenses/gpl.txt)