Mini-Batch Stochastic Approaches For Accelerated Multiplicative Updates In Nonnegative Matrix Factorisation With Beta-Divergence
Romain Serizel, LTCI, CNRS, TÚlÚcom ParisTech, UniversitÚ Paris - Saclay
Slim Essid, LTCI, CNRS, TÚlÚcom ParisTech, UniversitÚ Paris - Saclay
GaŰl Richard, LTCI, CNRS, TÚlÚcom ParisTech, UniversitÚ Paris - Saclay

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.