Semi-Supervised Logistic Discrimination via Regularized Gaussian Basis Expansions |
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Authors: | Shuichi Kawano Sadanori Konishi |
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Institution: | 1. Department of Mathematical Sciences, Graduate School of Engineering , Osaka Prefecture University , Osaka , Japan kawa.shuichi@gmail.com;3. Department of Mathematics, Faculty of Science and Engineering , Chuo University , Tokyo , Japan |
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Abstract: | The problem of constructing classification methods based on both labeled and unlabeled data sets is considered for analyzing data with complex structures. We introduce a semi-supervised logistic discriminant model with Gaussian basis expansions. Unknown parameters included in the logistic model are estimated by regularization method along with the technique of EM algorithm. For selection of adjusted parameters, we derive a model selection criterion from Bayesian viewpoints. Numerical studies are conducted to investigate the effectiveness of our proposed modeling procedures. |
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Keywords: | Bayesian approach EM algorithm Logistic regression Regularization Semi-supervised learning |
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