1994
@article{Com1994,
vgclass = {refpap},
author = {Pierre Comon},
title = {Independent component analysis, A new concept?},
journal = {Signal Processing},
volume = {36},
number = {3},
pages = {287--314},
month = {April},
year = {1994},
url = {http://dx.doi.org/10.1016/0165-1684(94)90029-9},
abstract = {The independent component analysis (ICA) of a random
vector consists of searching for a linear transformation that minimizes
the statistical dependence between its components. In order to define
suitable search criteria, the expansion of mutual information is
utilized as a function of cumulants of increasing orders. An efficient
algorithm is proposed, which allows the computation of the ICA of a
data matrix within a polynomial time. The concept of ICA may actually
be seen as an extension of the principal component analysis (PCA),
which can only impose independence up to the second order and,
consequently, defines directions that are orthogonal. Potential
applications of ICA include data analysis and compression, Bayesian
detection, localization of sources, and blind identification and
deconvolution.},
}