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In this paper we introduce functional discriminant analysis which is an extension of the classical method of logistic discriminant analysis to the data where predictor variables are functions or curve...s. The functional discriminant analysis approach can classify curves belong to two distinct classes effectively by imposing smoothness constraint on the predictor functions and coefficient function via regularized radial basis expansion. In order to select the number of basis functions to be expanded and the value of smoothing parameter which are essential in regularization, we derive an information criterion which enables us to evaluate model estimated by regularization. The proposed method is illustrated with the example in the analysis of yeast cell cycle microarray data. It is shown that functional discriminant analysis performs well especially in the sense of prediction accuracy.続きを見る
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