Asymptotic improvement of supervised learning by utilizing additional unlabeled samples: normal mixture density case

Behzad M. Shahshahani; David A. Landgrebe

doi:10.1117/12.130825

16 December 1992 Asymptotic improvement of supervised learning by utilizing additional unlabeled samples: normal mixture density case

Behzad M. Shahshahani, David A. Landgrebe

Author Affiliations +

Proceedings Volume 1766, Neural and Stochastic Methods in Image and Signal Processing; (1992) https://doi.org/10.1117/12.130825
Event: San Diego '92, 1992, San Diego, CA, United States

Abstract

The effect of additional unlabeled samples in improving the supervised learning process is studied in this paper. Three learning processes, supervised, unsupervised, and combined supervised-unsupervised, are compared by studying the asymptotic behavior of the estimates obtained under each process. Upper and lower bounds on the asymptotic covariance matrices are derived. It is shown that under a normal mixture density assumption for the probability density function of the feature space, the combined supervised-unsupervised learning is always superior to the supervised learning in achieving better estimates. Experimental results are provided to verify the theoretical concepts.

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Behzad M. Shahshahani and David A. Landgrebe "Asymptotic improvement of supervised learning by utilizing additional unlabeled samples: normal mixture density case", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); https://doi.org/10.1117/12.130825

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