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Generalized mixture models, semi-supervised learning, and unknown class inference

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Abstract

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 semi-supervised learning methods, we provide the generalization and discuss its computational implementation using three-stage expectation–maximization algorithm.

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Author information

Authors and Affiliations

  1. Department of Statistics, California Polytechnic State University, San Luis Obispo, CA, 93407, USA

    Samuel J. Frame

  2. Department of Statistics and Applied Probability, University of California, Santa Barbara, CA, 93106, USA

    Sreenivasa Rao Jammalamadaka

Authors
  1. Samuel J. Frame
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  2. Sreenivasa Rao Jammalamadaka
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Corresponding author

Correspondence to Samuel J. Frame.

Additional information

The first author is partially supported by NSF Grant ITR-03316979, US Air Force Grants FA8650-04-M-1659, F33615-98-C1210, and US Army Grant DAMD-17-02-C-0119. The second author is partially supported by NSF Grant ITR-03316979

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Frame, S.J., Jammalamadaka, S.R. Generalized mixture models, semi-supervised learning, and unknown class inference. ADAC 1, 23–38 (2007). https://doi.org/10.1007/s11634-006-0001-9

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  • DOI: https://doi.org/10.1007/s11634-006-0001-9

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