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Robust informed split gradient NMF using Alpha Beta-divergence for source apportionment

Abstract : Source apportionment is a very challenging topic for which non-negative source separation is well-suited. Recently, we proposed several informed Non-negative Matrix Factorization (NMF) for which some expert knowledge was introduced. These methods were all dealing with some set values of one factor together with the row sum-to-one property by either processing each constraint alternatingly or using a new parameterization which involves all of them. However, this last method was sensitive to the presence of outliers. In this pa- per, we thus propose a new robust informed Split Gradient NMF method which is based on a weighted alpha-beta divergence cost function. Experiments conducted for several input SNR with and without outliers on simulated mixtures of particulate matter sources show the relevance of the new approach.
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Contributor : Matthieu Puigt <>
Submitted on : Saturday, September 24, 2016 - 11:18:20 PM
Last modification on : Tuesday, January 5, 2021 - 1:04:02 PM




Robert Chreiky, Gilles Delmaire, Clément Dorffer, Matthieu Puigt, Gilles Roussel, et al.. Robust informed split gradient NMF using Alpha Beta-divergence for source apportionment. International Workshop on Machine Learning for Signal Processing (MLSP 2016), Sep 2016, Vietri Sul Mare, Italy. ⟨10.1109/MLSP.2016.7738843⟩. ⟨hal-01371238⟩



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