Semi-supervised logistic discrimination via labeled data and unlabeled data from different sampling distributions
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This article addresses the problem of classification method based on both labeled and unlabeled data, where we assume that a density function for labeled data is different from that for unlabeled data. We propose a semi-supervised logistic regression model for classification problem along with the technique of covariate shift adaptation. Unknown parameters involved in proposed models are estimated by regularization with EM algorithm. A crucial issue in the modeling process is the choices of tuning parameters in our semi-supervised logistic models. In order to select the parameters, a model selection criterion is derived from an information-theoretic approach. Some numerical studies show that our modeling procedure performs well in various cases.
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