Feature extraction using supervised Independent Component Analysis by maximizing class distance

Recently, Independent Component Analysis (ICA) has been applied to not only problems of blind signal separation, but also feature extraction of patterns. However, the effectiveness of features extracted by ICA (ICA features) has not been verified yet. As one of the reasons, it is considered that ICA features are obtained by increasing their independence rather than by increasing their class separability. Hence, we can expect that high-performance pattern features are obtained by introducing supervisor into conventional ICA algorithms such that the class separability of features is enhanced. In this work, we propose SICA by maximizing Mahalanobis distance between classes. Moreover, we propose a new distance measure in which each ICA feature is weighted by the power of principal components consisting of the ICA feature. In the recognition experiments, we demonstrate that the better recognition accuracy for two data sets in UCI Machine Learning Repository is attained when using features extracted by the proposed SICA.