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

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 Sh...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2014-01, Vol.62 (2), p.376-389
Main Authors: Ouedraogo, Wendyam Serge Boris, Souloumiac, Antoine, Jaidane, Meriem, Jutten, Christian
Format: Article
Language:English
Subjects:
aperture
Apertures
Applied sciences
Biological and medical sciences
Blind source separation
Computer Science
Computerized, statistical medical data processing and models in biomedicine
Detection, estimation, filtering, equalization, prediction
dynamic PET imaging
Exact sciences and technology
Image processing
Information, signal and communications theory
LC-MS
Mathematics
Medical management aid. Diagnosis aid
Medical sciences
non-negativity
Numerical Analysis
Positron emission tomography
Random variables
Signal and communications theory
Signal and Image Processing
Signal processing
Signal processing algorithms
Signal, noise
simplicial cone
Telecommunications and information theory
Vectors
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Summary: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.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2013.2287683