论文标题
使用距离相关的盲目确定来源数量
Blind Determination of the Number of Sources Using Distance Correlation
论文作者
论文摘要
提出了对嘈杂，线性混合物来源数量的新颖盲目估计。基于Székely等人的距离相关度量，我们定义了来自我们的估计值的依赖性标准（SDC）。与大多数先前提出的估计不同，SDC估计利用了来源和噪声的全部独立性，以及来源的非高斯性（与噪声的高斯性相反），通过隐式使用高级统计数据。这导致了更强大，弹性和稳定的估计W.R.T.混合矩阵和噪声协方差结构。经验模拟结果证明了这些美德，除了当前的最新状态估计外，除了出色的表现外。
A novel blind estimate of the number of sources from noisy, linear mixtures is proposed. Based on Székely et al.'s distance correlation measure, we define the Sources' Dependency Criterion (SDC), from which our estimate arises. Unlike most previously proposed estimates, the SDC estimate exploits the full independence of the sources and noise, as well as the non-Gaussianity of the sources (as opposed to the Gaussianity of the noise), via implicit use of high-order statistics. This leads to a more robust, resilient and stable estimate w.r.t. the mixing matrix and the noise covariance structure. Empirical simulation results demonstrate these virtues, on top of superior performance in comparison with current state of the art estimates.
