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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.
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Dates and versions

hal-01393964 , version 1 (08-11-2016)

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  • HAL Id : hal-01393964 , version 1

Cite

Romain Serizel, Slim Essid, Gael Richard. Mini-batch stochastic approaches for accelerated multiplicative updates in nonnegative matrix factorisation with beta-divergence. IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2016), Sep 2016, Salerne, Italy. ⟨hal-01393964⟩
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