In case of application to high-dimensional pattern recognition task, Independent Component Analysis (ICA) often suffers from two challenging problems. One is the small sample size problem. The other is the choice of basis functions (or independent components). Both problems make ICA classifier unstable and biased. To address the two problems, we propose an enhanced ICA algorithm using a cascaded ensemble learning scheme, named as Random Independent Subspace (RIS). A random resampling technique is used to generate a set of low dimensional feature subspaces in the original feature space and the whiten feature space, respectively. One classifier is constructed in each feature subspace. Then these classifiers are combined into an ensemble classifier using a final decision rule. Extensive experimentations performed on the FERET database suggest that the proposed method can improve the performance of ICA classifier.
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