ICA USING KERNEL CANONICAL CORRELATION ANALYSIS
Colin Fyfe and Pei Ling Lai
We derive a new method based on kernels for per-
forming Canonical Correlation Analysis. We show that
the method can be used to extract individual sinusoids
from a linear mixture of sinusoids and that this is also
possible when the number of mixtures is less than the
number of signals, when there is a nonlinear mixture of
the signals and when the mixture is time varying. In
the last case, the nature of the time varying mixture
matrix is revealed by some of the lower order canonical
correlations when we use a nonlinear kernel.