Liu Wei, Mandic Danilo P, Cichocki Andrzej
IEEE Trans Neural Netw. 2007 Sep;18(5):1505-10. doi: 10.1109/tnn.2007.894017.
A critical analysis of the canonical correlation analysis (CCA) approach in blind source separation (BSS) is provided. It is proved that by maximizing the autocorrelation functions of the recovered signals we can separate the source signals successfully. It is further shown that the CCA approach represents the same class of generalized eigenvalue decomposition (GEVD) problems as the matrix pencil method. Finally, online realizations of the CCA approach are discussed with a linear-predictor-based algorithm studied as an example.
对盲源分离（BSS）中典型相关分析（CCA）方法进行了批判性分析。证明了通过最大化恢复信号的自相关函数，我们可以成功分离源信号。进一步表明，CCA方法与矩阵束方法代表同一类广义特征值分解（GEVD）问题。最后，以一种基于线性预测器的算法为例，讨论了CCA方法的在线实现。
