Complex Random Vectors and ICA Models: Identifiability, Uniqueness and Separability
信息论
2011-11-09 v1 计算工程、金融与科学
信息检索
机器学习
math.IT
摘要
In this paper the conditions for identifiability, separability and uniqueness of linear complex valued independent component analysis (ICA) models are established. These results extend the well-known conditions for solving real-valued ICA problems to complex-valued models. Relevant properties of complex random vectors are described in order to extend the Darmois-Skitovich theorem for complex-valued models. This theorem is used to construct a proof of a theorem for each of the above ICA model concepts. Both circular and noncircular complex random vectors are covered. Examples clarifying the above concepts are presented.
引用
@article{arxiv.cs/0512063,
title = {Complex Random Vectors and ICA Models: Identifiability, Uniqueness and Separability},
author = {Jan Eriksson and Visa Koivunen},
journal= {arXiv preprint arXiv:cs/0512063},
year = {2011}
}
备注
To appear in IEEE TR-IT March 2006