Approximating the best linear unbiased estimator of non-gaussian signals with gaussian noise

Masashi Sugiyama, Motoaki Kawanabe, Gilles Blanchard, Klaus Robert Müller

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1577-1580
Number of pages4
JournalIEICE Transactions on Information and Systems
VolumeE91-D
Issue number5
DOIs
Publication statusPublished - 2008 May

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Keywords

  • Best linear unbiased estimator (BLUE)
  • Gaussian noise
  • Non-Gaussian component analysis (NGCA)
  • Signal denoising

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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