Obtaining the Best Linear Unbiased Estimator of Noisy Signals by Non-Gaussian Component Analysis
Obtaining the Best Linear Unbiased Estimator of Noisy Signals by Non-Gaussian Component Analysis
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DOI：
10.1109/icassp.2006.1660727
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发表时间：
2006-05
期刊：
影响因子：
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通讯作者：
Masashi Sugiyama;M. Kawanabe;G. Blanchard;V. Spokoiny;K. Müller
中科院分区：
文献类型：
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作者：
Masashi Sugiyama;M. Kawanabe;G. Blanchard;V. Spokoiny;K. Müller
获得噪声信号的最佳线性无偏估计（BLUE）是一种传统但强大的降噪方法。显式计算BLUE通常需要真实信号所属子空间和噪声协方差矩阵的先验知识。然而，这样的先验知识在现实中往往是不可用的，这阻碍了我们将BLUE应用于现实世界的问题。因此，在本文中，我们给出了一种在没有此类先验知识的情况下获得BLUE的方法。我们的附加假设是，真实信号遵循非高斯分布，而噪声是高斯分布
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