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

1996

@inproceedings{MaA1996,
	vgclass =	{refpap},
	vgproject =	{nn},
	author =	{Marques, Gon\,{c}alo and Lu\'{i}s B. Almeida},
	title =	{An Objective Function for Independence},
	booktitle =	{Proceedings of the IEEE International Conference on Neural
	Networks},
	year =	{1996},
	abstract =	{The problem of separating a linear or nonlinear mixture of
	independent sources has been the focus of many studies in recent years.
	It is well known that the classical principal components analysis
	method, which is based on second order statistics, performs poorly even
	in the linear case, if the sources do not have Gaussian distributions.
	Based on this fact, several algorithms take in account higher than
	second order statistics in their approach to the problem. Other
	algorithms use the Kullback-Leibler divergence to find a transformation
	that can separate the independent signals. Nevertheless, the great
	majority of these algorithms only take in account a finite number of
	statistics, usually up to the fourth order, or use some kind of
	smoothed approximations. In this paper we present a new class of
	objective functions for source separation. The objective functions use
	statistics of all orders simultaneously, and have the advantage of
	being continuous, differentiable functions that can be computed
	directly from the training data. A derivation of the class of functions
	for two dimensional data, some numerical examples illustrating its
	performance, and some implementation considerations are described.},
}