Feature extraction using ICA

Abstract

In manipulating data such as in supervised learning, we often extract new features from original features for the purpose of reducing the dimensions of feature space and achieving butter performances. In this paper, we propose a new feature extraction algorithm using independent component analysis (ICA) for classification problems. By using ICA in solving supervised classification problems, we can get new features which are made as independent from each other as possible and also convey the output information faithfully. Using the new features along with the conventional feature selection algorithms, we can greatly reduce the dimension of feature space without degrading the performance of classifying systems.