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ICA Based Neural Networks For Blind
Sources Separation
Student:
Ankoosh Jain
Project
Goal
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Development
of a two-layer neural network ICA architecture maximizing the entropy of the
outputs with logistic transfer function.
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Design of a
neural architecture for nonlinear three-layer ICA network for estimating the
respective basis vectors. They are counterparts of the PCA eigenvectors, but
characterize the data in many cases better. The purpose of the three layers is:
a)
whitening of the input data
for yielding good separation results;
b)
separation of independent sources (components);
c)
estimation of the basis vectors.
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