说明：  在信号处理中，独立成分分析（ICA）是一种用于将多元信号分离为加性子分量的计算方法。 [1] 这是通过假设子分量是非高斯信号，并且在统计上彼此独立来完成的。ICA是盲源分离的特例。一个常见的示例应用程序是在嘈杂的房间中聆听一个人的语音的“ 鸡尾酒会问题 ”。 [1] ICA(Independent Component Correlation Algorithm)是一种函数，X为n维观测信号矢量，S为独立的m（m<=n)维未知源信号矢量，矩阵A被称为混合矩阵。ICA的目的就是寻找解混矩阵W（A的逆矩阵），然后对X进行线性变换，得到输出向量U。
(In signal processing, independent component analysis (ICA) is a computing method used to separate multiple signals into additive sub components. [1] This is accomplished by assuming that the sub components are non Gaussian and statistically independent of each other. ICA is a special case of blind source separation. A common example application is the "cocktail party question" of listening to a person's voice in a noisy room. [1] ICA (independent component correlation algorithm) is a function. X is the n-dimensional observed signal vector, s is the independent m (m < = n) - dimensional unknown source signal vector, and matrix A is called mixed matrix. The purpose of ICA is to find the unmixing matrix w (the inverse matrix of a), and then transform x linearly to get the output vector U.)