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A new joint diagonalization algorithm with application in blind source separation

机译:一种新的联合对角化算法及其在盲源分离中的应用

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摘要

In this letter, we present a new nonorthogonal algorithm for joint diagonalization of a set of symmetric matrices. The algorithm alternates between updates of individual demixing matrix rows, and the update of each row is transferred to solving the eigenvector problem. By using some blind source separation simulations, we show that the algorithm obviously obtains an improved performance when the signal-to-noise ratio of the observed signals is relatively low.
机译:在这封信中,我们提出了一种新的非正交算法,用于对一组对称矩阵进行联合对角化。该算法在各个解混矩阵行的更新之间交替,并且将每一行的更新转移到解决特征向量问题。通过使用一些盲源分离仿真,我们表明,当所观察信号的信噪比相对较低时,该算法显然可以获得改进的性能。

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