A new method of feature extraction and its stability

In the classification on a high dimensional feature space such as in face recognition problems, feature extraction techniques are usually used to overcome the so called 'curse of dimensionality.' In this paper, we propose a new feature extraction method for classification problem based on the conventional independent component analysis. The local stability of the proposed method is also dealt with. The proposed algorithm makes use of the binary class labels to produce two sets of new features; one that does not carry information about the class label - these features will be discarded - and the other that does. The advantage is that general ICA algorithms become available to a task of feature extraction by maximizing the joint mutual information between class labels and new features, although only for two-class problems. Using the new features, we can greatly reduce the dimension of feature space without degrading the performance of classifying systems.