Maximum Likelihood and Covariant Algorithms
for
Independent Component Analysis
(abstract)
Maximum Likelihood and Covariant Algorithms
for
Independent Component Analysis
David J C MacKay
Bell and Sejnowski (1995) have derived a
blind signal processing algorithm for a
non-linear feedforward network from
an information maximization viewpoint.
This paper first shows that the same algorithm can be
viewed as a maximum likelihood algorithm for
the optimization of a
linear generative model.
Second, a covariant version of the algorithm is derived.
This algorithm is simpler and somewhat more biologically
plausible, involving no matrix inversions; and it converges
in a smaller number of iterations.
Third, this paper gives a partial proof of the
`folk-theorem' that any mixture of
sources with high-kurtosis histograms is separable by the
classic ICA algorithm.
Fourth, a collection of formulae are given that
may be useful for the adaptation of the non-linearity in the ICA algorithm.
postscript (Cambridge UK).
postscript (Canada mirror).
David MacKay's:
home page,
publications.
bibtex file.
Canadian mirrors:
home page,
publications.
bibtex file.