Designing Relevant Features for Continuous Data Sets Using ICA

Isolating relevant information and reducing the dimensionality of the original dataset are key areas of interest in pattern recognition and machine learning. In this paper a novel approach to reducing dimensionality of the feature space by employing independent component analysis (ICA) is introduced. While ICA is primarily a feature extraction technique, it is used here as a feature selection/construction technique in a generic way. The new technique, called FS_ICA, efficiently builds a reduced set of features without loss in accuracy and also has a fast incremental version. When used as a first step in supervised learning, FS_ICA outperforms comparable methods in efficiency without loss of classification accuracy. For large datasets as in medical image segmentation of High-Resolution Computer Tomography (HRCT) images, FS_ICA reduces dimensionality of the dataset substantially and results in efficient and accurate classification.