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PhD Thesis(Eurasip PhD Library)

Mini-batch stochastic approaches for accelerated multiplicative updates in nonnegative matrix factorisation with beta-divergence



Authors: Romain Serizel, Slim Essid, Gaë l Richard

Reference: in Proc. of IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Salerno, Italy, September 2016

Status: Accepted (International Conference)

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.

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