faxt_ica_example.m

利用MATLAB实现一维信号时间序列的

FastICA算法代码 
%对数据进行标准化 
close all,clear 
load np_all 
normsr=zscore(npp); 
features=normsr'; 
[r,c]= size(features); 
%对数据进行白化，使之为0均值，单位协方差 
features = features - mean(features')'*ones(1,c); 
[E, D]  = eig(cov(features',1)); 
V      = E*inv(sqrtm(D))*E'; 
z = V*features; 
%使用FastICA算法对数据进行特征提取.非线性函数选择tanh(a1y) 
epsilon=0.0001; 
m=r; 
p=1; 
W=zeros(r,1); 
for p=1:m 
W(:,p)=rand(r,1); 
W(:,p)=W(:,p)/norm(W(:,p)); 
exit=0; 
count=0; 
iter=1; 
while exit==0; 
count=count+1; 
temp=W(:,p); 
for i=1:m 
W(i,p)=mean(z(i,.*(tanh((temp)'*z)))-(mean(1-(tanh((temp))'*z).^2)).*temp(i,1); 
end 
ssum=zeros(r,1); 
for counter=1:p-1 
ssum=ssum+(W(:,p)'*W(:,counter))*W(:,counter); 
end 
W(:,p)=W(:,p)-ssum; 
W(:,p)=W(:,p)/norm(W(:,p)); 
if(abs((dot(W(:,p),temp)))<1+epsilon)&(abs((dot(W(:,p),temp)))>1-epsilon) 
exit=1; 
end 
iter=iter+1; 
end 
end 
W=W'; 
%找到最独立的方向 
power=sum(abs(W)'); 
[m, in]  = sort(power); 
W= W(in(r-2+1:r),; 
%计算新的特征数据. 
features = W*z; 
features=features'; 
X=features(find(kk==1|kk==2),; 
group=kk(find(kk==1|kk==2)); 
%画散点图. 
gscatter(X(:,1),X(:,2),group,'mr','o+')  
legend('health','npc-initial'); 
xlabel('1st component'); 
ylabel('2nd component');