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Abstract:
Data visualization and feature selection methods are proposed
based on the joint mutual information and ICA. The visualization
methods can find many good 2-D projections for high dimensional
data interpretation, which cannot be easily found by the other
existing methods. The new variable selection method is found to be
better in eliminating redundancy in the inputs than other methods
based on simple mutual information. The efficacy of the methods is
illustrated on a radar signal analysis problem to find 2-D viewing
coordinates for data visualization and to select inputs for a
neural network classifier.
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