|
Abstract:
We study the dynamics of a Hebbian ICA algorithm extracting a
single non-Gaussian component from a high-dimensional Gaussian
background. For both on-line and batch learning we find that a
surprisingly large number of examples are required to avoid
trapping in a sub-optimal state close to the initial conditions.
To extract a skewed signal at least
O
(
N
2
) examples are required for
N
-dimensional data and
O
(
N
3
) examples are required to extract a symmetrical signal with
non-zero kurtosis.
|
