Finite-sample analysis of impacts of unlabeled data and their labeling mechanisms in linear discriminant analysis |
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Authors: | Kenichi Hayashi Keiji Takai |
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Affiliation: | 1. Graduate School of Medicine, Osaka University, Osaka, Japan;2. Faculty of Commerce, Kansai University, Osaka, Japan |
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Abstract: | It is widely believed that unlabeled data are promising for improving prediction accuracy in classification problems. Although theoretical studies about when/how unlabeled data are beneficial exist, an actual prediction improvement has not been sufficiently investigated for a finite sample in a systematic manner. We investigate the impact of unlabeled data in linear discriminant analysis and compare the error rates of the classifiers estimated with/without unlabeled data. Our focus is a labeling mechanism that characterizes the probabilistic structure of occurrence of labeled cases. Results imply that an extremely small proportion of unlabeled data has a large effect on the analysis results. |
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Keywords: | Classification error Missing data Monte Carlo simulation Relative efficiency Semi-supervised learning |
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