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On the asymptotic improvement of supervised learning by utilizing additional unlabeled samples - Normal mixture density caseThe 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.
Document ID
19930048910
Document Type
Conference Paper
Authors
Shahshahani, Behzad M. (NASA Headquarters Washington, DC United States)
Landgrebe, David A. (Purdue Univ. West Lafayette, IN, United States)
Date Acquired
August 16, 2013
Publication Date
January 1, 1992
Publication Information
Publication: In: Neural and stochastic methods in image and signal processing; Proceedings of the Meeting, San Diego, CA, July 20-23, 1992 (A93-32905 12-63)
Subject Category
CYBERNETICS
Funding Number(s)
CONTRACT_GRANT: NAGW-925
Distribution Limits
Public
Copyright
Other