Independent Components of Natural Images under Variable
Neurocomputing, vol.49, no. 1-4, pp. 175-185.
A generalized ICA model allowing overcomplete bases and additive noises
in the observables is applied to natural image data.
It is well known that
such a model produces independent components that resemble
simple cells in primary visual cortex or Gabor functions.
We adopt a variable-sparsity density
on each independent component, given by the mixture of a delta function
and a standard Gaussian density.
In the experiment, we observe that
the aspect ratios of the optimal bases increase
with the noise level and the degree of sparsity.
The meaning of this phenomenon is discussed.