** Author: ** Erkki Oja (Helsinki University of Technology)

** Abstract: **
It is known that a linear autoencoder network (Baldi and
Hornik) can compute PCA. It is shown that using a nonlinearity
in the hidden layer, but keeping the weight matrices in the hidden and
output layers suitable constrained, will solve the problem of
independent component analysis and blind source separation, provided
that the input vector is a linear mixture of independent
sources. The learning rule is a nonlinear extension of the basic PCA
subspace rule. Several examples are shown.