Correlative components analysis for pattern classification

Abstract

Based upon a novel method named correlative components analysis, a simple but efficient pattern classification technique is proposed in this paper. Using this method, the relatively important components of high-dimensional pattern can be successfully identified, the original problem will be mapped onto a lower dimensional space, and therefore the complexity of a high-dimensional pattern classification problem will be substantially reduced. For comparing with the methods of sequential discriminant analysis and the principal component analysis, an example of classifying complex chemical information was used, and the results verified that the new method is reasonable in principle and successful in practice.