TY - GEN
T1 - Obtaining the best linear unbiased estimator of noisy signals by non-gaussian component analysis
AU - Sugiyama, M.
AU - Kawanabe, M.
AU - Blanchard, G.
AU - Spokoiny, V.
AU - Müller, K. R.
PY - 2006
Y1 - 2006
N2 - Obtaining the best linear unbiased estimator (BLUE) of noisy signals is a traditional but powerful approach to noise reduction. Explicitly computing 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 BLUE to real-world problems. In this paper, we therefore give a method for obtaining BLUE without such prior knowledge. Our additional assumption is that the true signal follows a non-Gaussian distribution while the noise is Gaussian.
AB - Obtaining the best linear unbiased estimator (BLUE) of noisy signals is a traditional but powerful approach to noise reduction. Explicitly computing 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 BLUE to real-world problems. In this paper, we therefore give a method for obtaining 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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M3 - Conference contribution
AN - SCOPUS:33847242174
SN - 142440469X
SN - 9781424404698
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - III608-III611
BT - 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
T2 - 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
Y2 - 14 May 2006 through 19 May 2006
ER -