Semi-supervised logistic discrimination for functional data

Details Semi-supervised logistic discrimination for functional data Semi-supervised logistic discrimination for functional data

2011-02-22

arxiv.org/abs/1102.4399v2

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Methodology

20 pages, 7 figures

Scientific paper

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

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