中文说明：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
English Description：
Independent component analysis is a powerful data analysis tool in recent years. In 1994, comon gave a more strict mathematical definition of ICA, which was first put forward by heranlt and jutten in 1986. Although ICA has not appeared for a long time, it has attracted more and more attention both in theory and application, and has become a hot research topic at home and abroad. Especially from the perspective of application, its application fields and application prospects are very broad. At present, it is mainly used in blind source separation, image processing, language recognition, communication, biomedical signal processing, brain functional imaging research, fault diagnosis, feature extraction, financial time series analysis and data mining. ICA is a method used to find hidden factors or components from multivariate (multidimensional) statistical data. It is considered as an extension of principal component analysis (PCA) and factor analysis. For blind source separation (BSS), ICA is an analysis process of separating or approximately separating the source signal without knowing the source signal, noise and mixing mechanism. -Independent Component Analysis, ICA