中文说明：独立成分分析(ICA,Independent Component Correlation Algorithm)简介 X＝AS 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。 U＝WX＝WAS 过程： (1)对输入数据进行中心化和白化预处理 X*=X-u 经过白化变换后的样本数据为 Z＝Wz X* (2)从白化样本中求解出解混矩阵W 通过优化目标函数的方法得到W (3)得到独立的基向量U U＝WX
English Description：
Independent component analysis (ICA, Independent Component Correlation Algorithm) IntroductionX = ASX is an n-dimensional vector of the observed signal, S is independent of m (m <= n) dimensional unknown source signal vector, matrix A is called the mixing matrix.The purpose is to find ICA unmixing matrix W (A inverse matrix), then the linear transformation of X, to obtain the output vector U.U = WX = WASProcess:(1) the input data centers and albino pretreatmentX * = X-uAfter whitening the transformed sample data forZ = Wz X *(2) to solve the unmixing matrix W samples from albinoW obtained by the method of optimizing the objective function(3) by an independent basis vectors UU = WX