Data Visualization and Feature Selection: New Algorithms for Nongaussian Data

Abstract

Data visualization and feature selection methods are proposed based on the )oint 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 ex(cid:173) isting 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. Keywords: feature selection, joint mutual information, ICA, vi(cid:173) sualization, classification.