Non-Negative Blind Source Separation Algorithm Based on Minimum Aperture Simplicial Cone

Wendyam Ouedraogo 1 Antoine Souloumiac 2 Mériem Jaidane 3 Christian Jutten 1
1 GIPSA-VIBS - VIBS
GIPSA-DIS - Département Images et Signal
2 LADIS - Laboratoire d'analyse des données et d'intelligence des systèmes
DM2I - Département Métrologie Instrumentation & Information : DRT/LIST/DM2I
Abstract : We address the problem of Blind Source Separation (BSS) when the hidden sources are Nonnegative (N-BSS). In this case, the scatter plot of the mixed data is contained within the simplicial cone generated by the columns of the mixing matrix. The proposed method, termed SCSA-UNS for Simplicial Cone Shrinking Algorithm for Unmixing Non-negative Sources, aims at estimating the mixing matrix and the sources by fitting a Minimum Aperture Simplicial Cone (MASC) to the cloud of mixed data points. SCSA-UNS is evaluated on both independent and correlated synthetic data and compared to other N-BSS methods. Simulations are also performed on real Liquid Chromatography-Mass Spectrum (LC-MS) data for the metabolomic analysis of a chemical sample, and on real dynamic Positron Emission Tomography (PET) images, in order to study the pharmacokinetics of the [18F]-FDG (FluoroDeoxyGlucose) tracer in the brain.
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Wendyam Ouedraogo, Antoine Souloumiac, Mériem Jaidane, Christian Jutten. Non-Negative Blind Source Separation Algorithm Based on Minimum Aperture Simplicial Cone. IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2014, 62 (2), pp.376-389. ⟨10.1109/TSP.2013.2287683⟩. ⟨hal-00960232⟩

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