Classification via principal differential analysis

초록

We propose principal di erential analysis based classification methods. Computations of squared multiple correlation function (RSQ) and principal di erential analysis (PDA) scores are reviewed; in addition, we combine principal di erential analysis results with the logistic regression for binary classification. In the numerical study, we compare the principal di erential analysis based classification methods with functional principal component analysis based classification. Various scenarios are considered in a simulation study, and principal di erential analysis based classification methods classify the functional data well. Gene expression data is considered for real data analysis. We observe that the PDA score based method also performs well.

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