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|Title:||Robust informed split gradient NMF using Alpha ß-divergence for source apportionment||Authors:||Chreiky, Robert
Roussel , Gilles
|Affiliations:||Department of Electrical Engineering||Keywords:||Matrix algebra
|Issue Date:||2016||Publisher:||IEEE||Part of:||IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), 2016||Start page:||1||End page:||6||Conference:||IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) (13-16 Sept. 2016 : Vietri sul Mare, Italy)||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 paper, we thus propose a new robust informed Split Gradient NMF method which is based on a weighted αβ-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.
|URI:||https://scholarhub.balamand.edu.lb/handle/uob/796||Ezproxy URL:||Link to full text||Type:||Conference Paper|
|Appears in Collections:||Department of Electrical Engineering|
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