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Enhanced NMF initialization using a physical model for pollution source apportionment

Abstract : In a previous work, we proposed an informed Non-negative Matrix Factorization (NMF) with a specific parametrization which involves constraints about some known components of the factorization. In this paper we extend the above work by adding some information provided by a physical dispersion model. In particular, we derive a special structure of one of the factorizing matrices, which provides a better initialization of the NMF procedure. Experiments on simulated mixtures of particulate matter sources show that our new approach outperforms both our previous one and the state-of-the-art NMF methods.
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Contributor : Matthieu Puigt <>
Submitted on : Thursday, September 15, 2016 - 10:37:59 PM
Last modification on : Tuesday, January 5, 2021 - 1:04:02 PM
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  • HAL Id : hal-01367324, version 1



Marc Plouvin, Abdelhakim Limem, Matthieu Puigt, Gilles Delmaire, Gilles Roussel, et al.. Enhanced NMF initialization using a physical model for pollution source apportionment. 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2014), Apr 2014, Brugge, Belgium. pp.261-266. ⟨hal-01367324⟩



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