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独立成分分析ICA

于 2023-09-09 发布文件大小:430.00 kB

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独立成分分析是近年来出现的一种强有力的数据分析工具。1994年由Comon给出了ICA的一个较为严格的数学定义，其思想最早是由Heranlt和Jutten于1986年提出来的。ICA从出现到现在虽然时间不长，然而无论从理论上还是应用上，它正受到越来越多的关注，成为国内外研究的一个热点。特别是从应用角度看，它的应用领域与应用前景都是非常广阔的，目前主要应用于盲源分离、图像处理、语言识别、通信、生物医学信号处理、脑功能成像研究、故障诊断、特征提取、金融时间序列分析和数据挖掘等。 ICA是一种用来从多变量（多维）统计数据里找到隐含的因素或成分的方法，被认为是主成分分析（Principal Component Analysis, PCA）和因子分析（Factor Analysis）的一种扩展。对于盲源分离问题，ICA是指在只知道混合信号，而不知道源信号、噪声以及混合机制的情况下，分离或近似地分离出源信号的一种分析过程。-Independent Component Analysis, ICA

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