From blind signal extraction to blind instantaneous signal separation: Criteria, algorithms, and stability
From blind signal extraction to blind instantaneous signal separation: Criteria, algorithms, and stability
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DOI：
10.1109/tnn.2004.828764
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发表时间：
2004-07-01
影响因子：
--
通讯作者：
Amari, SI
中科院分区：
文献类型：
--
作者：
Cruces-Alvarez, SA;Cichocki, A;Amari, SI
本文报道了从线性混合物中盲同时提取特定组独立成分问题的研究。本文首先介绍了一个一般性的概述和统一的几个信息论标准的提取一个单一的独立成分。然后，我们的贡献填补了提取和分离之间存在的理论空白，提出的工具，扩展这些标准，允许同时盲提取的子集与任意数量的独立组件。此外，我们分析了一个家庭的学习算法的基础上Stiefel流形和自然梯度上升，提出了非线性最佳激活（得分）功能，并提供新的或扩展的局部稳定性条件。最后，我们通过计算机仿真实验说明了所提出的方法的性能和特点。
This paper reports a study on the problem of the blind simultaneous extraction of specific groups of independent components from a linear mixture. This paper first presents a general overview and unification of several information theoretic criteria for the extraction of a single independent component. Then, our contribution fills the theoretical gap that exists between extraction and separation by presenting tools that extend these criteria to allow the simultaneous blind extraction of subsets with an arbitrary number of independent components. In addition, we analyze a family of learning algorithms based on Stiefel manifolds and the natural gradient ascent, present the nonlinear optimal activations (score) functions, and provide new or extended local stability conditions. Finally, we illustrate the performance and features of the proposed approach by computer-simulation experiments.