Obtaining the best linear unbiased estimator of noisy signals by non-gaussian component analysis

M. Sugiyama, M. Kawanabe, G. Blanchard, V. Spokoiny, Klaus Muller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume3
Publication statusPublished - 2006 Dec 1
Externally publishedYes
Event2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France
Duration: 2006 May 142006 May 19

Other

Other2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
CountryFrance
CityToulouse
Period06/5/1406/5/19

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Acoustics and Ultrasonics

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  • Cite this

    Sugiyama, M., Kawanabe, M., Blanchard, G., Spokoiny, V., & Muller, K. (2006). Obtaining the best linear unbiased estimator of noisy signals by non-gaussian component analysis. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (Vol. 3). [1660727]