Abstract

Discriminant analysis based on Gaussian mixture models, an useful tool for multi-class classifications, can be extended to semi-supervised learning. We consider a model selection problem for a Gaussian mixture model in semi-supervised learning. More specifically, we adopt Bayesian information criterion to determine the number of subclasses in the mixture model. Through simulations, we illustrate the usefulness of the criterion.

Keywords

• BIC;
• classification;
• density estimation;
• EM algorithm;
• Gaussian mixture

• 베이지안 정보량;
• 분류;
• 밀도 추정;
• EM 알고리즘;
• 정규 혼합분포;

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References

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