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Title: Robust informed split gradient NMF using Alpha ß-divergence for source apportionment
Authors: Chreiky, Robert
Delamire, Gilles
Dorerr, Clement
Puigt, Matthieu
Roussel , Gilles
Abche, Antoine 
Affiliations: Department of Electrical Engineering 
Keywords: Matrix algebra
Computational geometry
Gradient methods
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) 
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.
Ezproxy URL: Link to full text
Type: Conference Paper
Appears in Collections:Department of Electrical Engineering

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