Joint Blind Source Separation by Multi-set Canonical Correlation Analysis.
Joint Blind Source Separation by Multi-set Canonical Correlation Analysis.
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DOI：
10.1109/tsp.2009.2021636
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发表时间：
2009-10-01
期刊：
影响因子：
--
通讯作者：
Calhoun VD
中科院分区：
文献类型：
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作者：
Li YO;Adalı T;Wang W;Calhoun VD
本文提出了一种简单有效的多数据集联合盲源分离（BSS）方案，利用多集典型相关分析（M-CCA）实现多数据集联合盲源分离。我们首先提出了一种基于数据集内部和数据集之间潜在源相关性的联合BSS生成模型。我们指定了源可分离性条件，并证明了当条件满足时，M-CCA可以通过最大化被提取源之间的相关性来联合提取每个数据集的对应源组。我们将M-CCA方案的源分离性能与其他联合BSS方法进行了比较，并证明了M-CCA方案在实现大量数据集、具有异构相关值的对应源组以及具有圆形和非圆形分布的复值源的联合BSS方面具有优越的性能。我们将M-CCA应用于分析来自多个受试者的功能磁共振成像（fMRI）数据，并显示其在估计视觉运动任务中有意义的大脑激活方面的效用。
In this work, we introduce a simple and effective scheme to achieve joint blind source separation (BSS) of multiple datasets using multi-set canonical correlation analysis (M-CCA). We first propose a generative model of joint BSS based on the correlation of latent sources within and between datasets. We specify source separability conditions, and show that, when the conditions are satisfied, the group of corresponding sources from each dataset can be jointly extracted by M-CCA through maximization of correlation among the extracted sources. We compare source separation performance of the M-CCA scheme with other joint BSS methods and demonstrate the superior performance of the M-CCA scheme in achieving joint BSS for a large number of datasets, group of corresponding sources with heterogeneous correlation values, and complex-valued sources with circular and non-circular distributions. We apply M-CCA to analysis of functional magnetic resonance imaging (fMRI) data from multiple subjects and show its utility in estimating meaningful brain activations from a visuomotor task.