paper available
Luis B. Almeida
lba at inesc.pt
Tue May 28 11:26:09 EDT 1996
The following paper, which will appear in the Proceedings of the IEEE
International Conference on Neural Networks 1996, Washington DC, June
1996, is available at
ftp://aleph.inesc.pt/pub/lba/icnn96.ps
An Objective Function for Independence
Goncalo Marques and Luis B. Almeida
IST and INESC, Lisbon, Portugal
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 component 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.
In this electronic version a few typos of the printed version have been
corrected.
The paper is reproduced with permission from the IEEE. Please read the
copyright notice at the beginning of the document.
--
Luis B. Almeida
INESC Phone: +351-1-3544607, +351-1-3100246
R. Alves Redol, 9 Fax: +351-1-3145843
P-1000 Lisboa
Portugal
e-mail: lba at inesc.pt or luis.almeida at inesc.pt
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