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.
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.