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

2000

@article{DDV2000,
	vgclass =	{refpap},
	author =	{Lieven De Lathauwer, Lieven and De Moor, Bart and
	Vandewalle, Joos},
	title =	{An introduction to independent component analysis},
	journal =	{Journal of Chemometrics},
	volume =	{14},
	number =	{3},
	pages =	{123--149},
	year =	{2000},
	url =	{http://dx.doi.org/10.1002/1099-128X(200005/06)14:3<123::AID-CEM589>3.0.CO;2-1},
	abstract =	{This paper is an introduction to the concept of
	independent component analysis (ICA) which has recently been developed
	in the area of signal processing. ICA is a variant of principal
	component analysis (PCA) in which the components are assumed to be
	mutually statistically independent instead of merely uncorrelated. The
	stronger condition allows one to remove the rotational invariance of
	PCA, i.e. ICA provides a meaningful unique bilinear decomposition of
	two-way data that can be considered as a linear mixture of a number of
	independent source signals. The discipline of multilinear algebra
	offers some means to solve the ICA problem. In this paper we briefly
	discuss four orthogonal tensor decompositions that can be interpreted
	in terms of higher-order generalizations of the symmetric eigenvalue
	decomposition.},
}