Pattern Recognition Method Using Independent Components for Each Class

Applications of independent component analysis (ICA) to feature extraction have been a topic of research interest. However, the effectiveness of pattern features extracted by conventional ICA algorithms greatly depends on datasets in general. As one of the reasons, we have pointed out that conventional ICA features are obtained by increasing only their independence even if class information is available. In this paper, we propose a supervised learning approach to ICA to extract useful and robust features. The proposed method consists of several modules, each of which is responsible for extracting features for each class and identifying the class labels using the k nearest neighbor classifier. All the module outputs are combined to identify final results based on a majority rule. We evaluate the performance of the proposed method in several recognition tasks. From these results, we confirm the effectiveness of the recognition method using independent components for each class.