Variational and stochastic inference for Bayesian source separation
Digital Signal Processing
Bayesian Non-negative Matrix Factorization
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Ion-Selective Electrode Array Based on a Bayesian Nonlinear Source Separation Method
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Kernel bandwidth estimation for nonparametric modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery
IEEE Transactions on Signal Processing
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Semi-nonnegative independent component analysis: the (3,4)-SENICAexpmethod
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Unmixing of Hyperspectral Images using Bayesian Non-negative Matrix Factorization with Volume Prior
Journal of Signal Processing Systems
Bayesian Source Separation of Linear and Linear-quadratic Mixtures Using Truncated Priors
Journal of Signal Processing Systems
Sampling normal distribution restricted on multiple regions
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
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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 are 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