期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 54, 期 11, 页码 4133-4145出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2006.880310
关键词
Bayesian estimation; Gamma distribution; Markov chain Monte Carlo (MCMC); non-negativity constraint; source separation; spectroscopy
This paper addresses blind-source separation in the case where both the source signals and the mixing coefficients are non-negative. The problem is referred to as non-negative source separation and the main application concerns the analysis of spectrometric data sets. The separation is performed in a Bayesian framework by encoding non-negativity through the assignment of Gamma priors on the distributions of both the source signals and the mixing coefficients. A Markov chain Monte Carlo (MCMC) sampling procedure is proposed to simulate the resulting joint posterior density from which marginal posterior mean estimates of the source signals and mixing coefficients ate obtained. Results obtained with synthetic and experimental spectra are used to discuss the problem of non-negative source separation and to illustrate the effectiveness of the proposed method.
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