Refereed full papers (journals, book chapters, international conferences)

1995

@article{BeS1995,
	vgclass =	{refpap},
	author =	{Anthony J. Bell and Terrence J. Sejnowski},
	title =	{An information-maximization approach to blind separation
	and blind deconvolution},
	journal =	{Neural Computation},
	volume =	{7},
	number =	{6},
	pages =	{1129--1159},
	month =	{November},
	year =	{1995},
	url =	{http://citeseer.ist.psu.edu/bell95informationmaximization.html},
	abstract =	{Derives a self-organizing learning algorithm that
	maximizes the information transferred in a network of nonlinear units.
	The nonlinearities in the transfer function pick up higher-order
	moments of the input distributions and perform something akin to true
	redundancy reduction between units in the output representation. This
	enables the network to separate statistically independent components in
	the inputs: a higher-order generalization of principal components
	analysis. The network is applied to the source separation ("cocktail
	party") problem, successfully separating unknown mixtures of up to 10
	speakers. It is shown that a variant on the network architecture can
	perform blind deconvolution. Dependencies of information transfer on
	time delays are derived. Information maximization appears to provide a
	unifying framework for problems in blind signal processing.},
}