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Some results on constrained maximum likelihood estimation

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1 Author(s)
Y. Kamp ; Philips Research Laboratory, Brussels, Belgium

This paper considers, for a multivariate Gaussian random process, the maximum likelihood estimation (MLE) of a covariance matrix whose structure satisfies some particular constraints. First, one examines the case where the random process is required to satisfy a time varying auto-regressive (AR) model of fixed order p. In particular, one shows that the resulting optimal covariance matrix is a partial reconstruction of the given sample covariance matrix. Next, a linear feature extraction is considered with a slightly unusual criterion which requires that the likelihood of the extracted features should be as large as possible.

Published in:

Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '86.  (Volume:11 )

Date of Conference:

Apr 1986