Generalized Mixture Models, Semi-supervised Learning, and Unknown Class Inference

Samuel J. Frame1
California Polytechnic State University
Department of Statistics
San Luis Obispo, CA. 93407
S. Rao Jammalamadaka2
Department of Statistics and Applied Probability
University of California
Santa Barbara, CA. 93106


In this paper, we discuss Generalized Mixture Models and related Semi-supervised learning methods, and show how they can be used to provide explicit methods for unknown class inference. After a brief description of standard mixture modeling and current model-based semisupervised learning methods, we provide the generalization and discuss its computational implementation using 3-stage Expectation-Maximization algorithm.
[PDF] [BibTex]
Samuel Frame and S. Rao Jammalamadaka,
Advances in Data Analysis and Classification, Springer, 2007.
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