PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Approximating the Best Linear Unbiased Estimator of Non-Gaussian Signals with Gaussian Noise
Masashi Sugiyama, Motoaki Kawanabe, Gilles Blanchard and Klaus-Robert Müller
IEICE Transactions on Information and Systems Volume E91-D, Number 5, pp. 1577-1580, 2008. ISSN 0916-8532 (print) 1745-1361 (online)

Abstract

Obtaining the best linear unbiased estimator (BLUE) of noisy signals is a traditional but powerful approach to noise reduction. Explicitly computing the BLUE usually requires the prior knowledge of the noise covariance matrix and the subspace to which the true signal belongs. However, such prior knowledge is often unavailable in reality, which prevents us from applying the BLUE to real-world problems. To cope with this problem, we give a practical procedure for approximating the BLUE without such prior knowledge. Our additional assumption is that the true signal follows a non-Gaussian distribution while the noise is Gaussian.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4417
Deposited By:Gilles Blanchard
Deposited On:13 March 2009