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Subspace learning and innovation characterization in generalized Gaussian noise

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2 Author(s)
Desai, M. ; Draper (C.S.) Lab., Cambridge, MA, USA ; Mangoubi, R.

This paper formulates the problem of maximum likelihood subspace learning and innovation characterization in the presence of generalized Gaussian noise. This approach leads to a set of necessary conditions that are a nonlinear generalization of the Gaussian eigenvalue decomposition of the sample covariance matrix. To address the innovation problem, a class of jointly generalized Gaussian random variables is introduced using a generalized correlation matrix. Necessary condition for the maximum likelihood estimate of that matrix are derived, whose solution would permit the recovery of the innovation.

Published in:

Signals, Systems and Computers, 2004. Conference Record of the Thirty-Seventh Asilomar Conference on  (Volume:2 )

Date of Conference:

9-12 Nov. 2003