SEMI-SUPERVISED LOGISTIC DISCRIMINATION FOR FUNCTIONAL DATA

Multi-class classification methods based on both labeled and unlabeled functional data sets are discussed. We present a semi-supervised logistic model for classification in the context of functional data analysis. Unknown parameters in our proposed model are estimated by regularization with the help of EM algorithm. A crucial point in the modeling procedure is the choice of a regularization parameter involved in the semi-supervised functional logistic model. In order to select the adjusted parameter, we introduce model selection criteria from information-theoretic and Bayesian viewpoints. Monte Carlo simulations and a real data analysis are given to examine the effectiveness of our proposed modeling strategy.