Independent Vector Analysis: Identification Conditions and Performance Bounds
Independent Vector Analysis: Identification Conditions and Performance Bounds
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DOI：
10.1109/tsp.2014.2333554
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发表时间：
2014-09-01
影响因子：
5.4
通讯作者：
Adali, Tulay
中科院分区：
文献类型：
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作者：
Anderson, Matthew;Fu, Geng-Shen;Adali, Tulay
最近，将独立组件分析（ICA）从一个数据集扩展到多个数据集，称为独立矢量分析（IVA），一直是重要研究兴趣的主题。 IVA也已被证明是Hotelling的规范相关分析的概括。在本文中，我们提供了一般IVA公式的识别条件，该条件是线性，非线性和样本对样本依赖性的说明。当样本是独立和相同分布时，识别条件是ICA和IVA的先前结果的概括。此外，IVA的主要目的是识别数据集之间的依赖源。因此，我们为估计来源的任意排序何时在数据集中很常见。还提供了将矩阵矩阵和干扰与源比的切解的绩效界限。将两种IVA算法的性能与理论界限进行了比较。
Recently, an extension of independent component analysis (ICA) from one to multiple datasets, termed independent vector analysis (IVA), has been a subject of significant research interest. IVA has also been shown to be a generalization of Hotelling's canonical correlation analysis. In this paper, we provide the identification conditions for a general IVA formulation, which accounts for linear, nonlinear, and sample-to-sample dependencies. The identification conditions are a generalization of previous results for ICA and for IVA when samples are independently and identically distributed. Furthermore, a principal aim of IVA is identification of dependent sources between datasets. Thus, we provide additional conditions for when the arbitrary ordering of the estimated sources can be common across datasets. Performance bounds in terms of the Cram r-Rao lower bound are also provided for demixing matrices and interference to source ratio. The performance of two IVA algorithms are compared to the theoretical bounds.