ICA人脸识别算法实例matlab源码.docVIP

ICA人脸识别算法实例matlab源码 ICA人脸识别算法实例matlab源码 PAGE ICA人脸识别算法实例matlab源码 ICA人脸识别算法实例matlab源码.txt32因为爱心，流浪的人们才能重返家园；因为爱心，疲惫的灵魂才能活力如初。渴望爱心，如同星光渴望彼此辉映；渴望爱心，如同世纪之歌渴望永远被唱下去。%%%%%%%%%%%%%%%%%%%%%%%%%% 初始化 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% clc;clear all;close all; %%%%%%%%%%%%%% 读入原始图像，混合，并输出混合图像 %%%%%%%%%%%%%%%%%% % 读入混合前的原始图片并显示 t=0:1/100:9; I1=sin(t); I2=randn(1,901); I3=square(4*t); subplot(4,3,1),plot(I1),title(输入信号1), subplot(4,3,2),plot(I2),title(输入信号2), subplot(4,3,3),plot(I3),title(输入信号3), % 将其组成矩阵 S=[I1;I2;I3]; % 图片个数即为变量数，图片的像素数即为采样数 % 因此S_all是一个变量个数＊采样个数的矩阵 Sweight=randn(size(S,1)); % 取一随机矩阵，作为信号混合的权矩阵 MixedS=Sweight*S; % 得到三个混合信号矩阵 % 将混合矩阵重新排列并输出 subplot(4,3,4),plot(MixedS(1,:)),title(混合信号1), subplot(4,3,5),plot(MixedS(2,:)),title(混合信号2), subplot(4,3,6),plot(MixedS(3,:)),title(混合信号3), MixedS_bak=MixedS; % 将混合后的数据备份，以便在恢复时直接调用 %%%%%%%%%%%%%%%%%%%%%%%%%% 标准化 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% MixedS_mean=zeros(3,1); for i=1:3 MixedS_mean(i)=mean(MixedS(i,:)); end % 计算MixedS的均值 for i=1:3 for j=1:size(MixedS,2) MixedS(i,j)=MixedS(i,j)-MixedS_mean(i); end end %%%%%%%%%%%%%%%%%%%%%%%%%%% 白化 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% MixedS_cov=cov(MixedS); % cov为求协方差的函数 [E,D]=eig(MixedS_cov); % 对图片矩阵的协方差函数进行特征值分解 Q=inv(sqrt(D))*(E); % Q为白化矩阵 MixedS_white=Q*MixedS; % MixedS_white为白化后的图片矩阵 IsI=cov(MixedS_white); % IsI应为单位阵 %%%%%%%%%%%%%%%%%%%%%%%%　FASTICA算法 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% X=MixedS_white; % 以下算法将对X进行操作 [VariableNum,SampleNum]=size(X); numofIC=VariableNum; % 在此应用中，独立元个数等于变量个数 B=zeros(numofIC,VariableNum);