Latent variable models and factors analysis
Latent variable models and factors analysis
Applied multivariate statistical analysis
Applied multivariate statistical analysis
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
High-order contrasts for independent component analysis
Neural Computation
A unifying review of linear Gaussian models
Neural Computation
Independent component analysis: algorithms and applications
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A Bayesian blind source separation (BSS) algorithm is proposed in this paper to recover independent sources from observed multivariate spatial patterns. As a widely used mechanism, Gaussian mixture model is adopted to represent the sources for statistical description and machine learning. In the context of linear latent variable BSS model, some conjugate priors are incorporated into the hyperparameters estimation of mixing matrix. The proposed algorithm then approximates the full posteriors over model structure and source parameters in an analytical manner based on variational Bayesian treatment. Experimental studies demonstrate that this Bayesian source separation algorithm is appropriate for systematic spatial pattern analysis by modeling arbitrary sources and identify their effects on high dimensional measurement data. The identified patterns will serve as diagnosis aids for gaining insight into the nature of physical process for the potential use of statistical quality control.