PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Obtaining the best linear unbiased estimator of noisy signals by non-gaussian component analysis
Motoaki Kawanabe, Gilles Blanchard, Masashi Sugiyama, Vladimir Spokoiny and Klaus-Robert Müller
In: ICASSP 06, 15-19 May 2006, Toulouse, France.

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 subspace to which the true signal belongs and the noise covariance matrix. However, such prior knowledge is often unavailable in reality, which prevents us from applying the BLUE to real-world problems. In this paper, we present a method for approaching the BLUE without such prior knowledge. Our additional assumption is that the true signal follows a non-Gaussian distribution while the noise is Gaussian.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2534
Deposited By:Gilles Blanchard
Deposited On:22 November 2006