Analysis and online realization of the CCA approach for blind source separation
Analysis and online realization of the CCA approach for blind source separation
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DOI：
10.1109/tnn.2007.894017
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发表时间：
2007-09-01
影响因子：
--
通讯作者：
Cichocki, Andrzej
中科院分区：
文献类型：
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作者：
Liu, Wei;Mandic, Danilo P.;Cichocki, Andrzej
提供了盲源分离（BSS）中典型相关分析（CCA）方法的关键分析。事实证明，通过最大化恢复信号的自相关函数，我们可以成功地分离源信号。进一步表明，CCA 方法代表了与矩阵铅笔方法相同类别的广义特征值分解 (GEVD) 问题。最后，以基于线性预测器的算法为例，讨论了 CCA 方法的在线实现。
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.