On Optimal Pairwise Linear Classifiers for Normal Distributions: The Two-Dimensional Case
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A new method for two-class linear discriminant analysis, called "removal of classification structure," is proposed. Its novelty lies in the transformation of the data along an identified discriminant direction into data without discriminant information and iteration to obtain the next discriminant direction. It is free to search for discriminant directions oblique to each other and ensures that the informative directions already found will not be chosen again at a later stage. The efficacy of the method is examined for two discriminant criteria. Studies with a wide spectrum of synthetic data sets and a real data set indicate that the discrimination quality of these criteria can be improved by the proposed method.