Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Learning in graphical models
An introduction to variational methods for graphical models
Learning in graphical models
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Neural Computation
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Independent Component Analysis: Principles and Practice
Independent Component Analysis: Principles and Practice
Bayesian parameter estimation via variational methods
Statistics and Computing
Variational mixture of Bayesian independent component analyzers
Neural Computation
Hierarchy, priors and wavelets: structure and signal modelling using ICA
Signal Processing - Special issue on independent components analysis and beyond
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
A Bayesian inverse solution using independent component analysis
Neural Networks
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In many data-driven machine learning problems it is useful to consider the data as generated from a set of unknown (latent) generators or sources. The observations we make are then taken to be related to these sources through some unknown functionaility. Furthermore, the (unknown) number of underlying latent sources may be different to the number of observations and hence issues of model complexity plague the analysis. Recent developments in Independent Component Analysis (ICA) have shown that, in the case where the unknown function linking sources to observations is linear, data decomposition may be achieved in a mathematically elegant manner. In this paper we extend the general ICA paradigm to include a very flexible source model and prior constraints and argue that for particular biomedical signal processing problems (we consider EEG analysis) we require the constraint of positivity in the mixing process.