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Sparse representation and blind source separation of ill-posed mixtures

机译:不良混合物的稀疏表示和盲源分离

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Bofill et al. discussed blind source separation (BSS) of sparse signals in the case of two sensors. However, as Bofill et al. pointed out, this method has some limitation. The potential function they introduced is lack of theoretical basis. Also the method could not be extended to solve the problem in the case of more than three sensors. In this paper, instead of the potential function method, a K-PCA method (combining K-clustering with PCA) is proposed. The new method is easy to be used in the caseof more than three sensors. It is easy to be implemented and can provide accurate estimation of mixing matrix. Some criterion is given to check the effect of the mixing matrix A. Some simulations illustrate the availability and accuracy of the method weproposed.
机译:Bofill等。讨论了在两个传感器的情况下稀疏信号的盲源分离(BSS)。但是,正如Bofill等。指出,这种方法有一定的局限性。他们介绍的潜在功能缺乏理论基础。同样,该方法不能扩展为解决三个以上传感器的问题。在本文中,代替势函数方法,提出了一种K-PCA方法(将K聚类与PCA结合)。在三个以上的传感器的情况下,该新方法易于使用。它易于实现并且可以提供混合矩阵的准确估计。给出了一些标准来检查混合矩阵A的效果。一些仿真说明了我们提出的方法的可用性和准确性。

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