RobustICAmatlab代码
时间: 2023-07-09 12:29:26 浏览: 37
以下是MATLAB语言实现的RobustICA代码示例:
``` matlab
function [S, W] = robust_ica(X, max_iter, tol)
% X: mixed signal matrix, each row represents a mixed signal
% max_iter: maximum number of iterations
% tol: tolerance for stopping criterion
% S: estimated source signal matrix
% W: estimated mixing matrix
[n, m] = size(X);
W = eye(n);
for i = 1:max_iter
W_prev = W;
% 直接使用FastICA进行ICA分离
[S, A, W] = fastica(X, 'maxNumIterations', 1, 'initGuess', W');
% 计算当前估计和上一次估计之间的误差
diff = norm(abs(W * W_prev') - eye(n), 'fro');
if diff < tol
break;
end
end
```
该示例代码使用了MATLAB中的FastICA算法,直接调用了MATLAB内置函数`fastica`进行ICA分离。在RobustICA中,我们可以使用多次FastICA迭代来提高鲁棒性。这里的代码将多次迭代FastICA,并且在每次迭代中更新分离矩阵。如果两次迭代之间的分离矩阵之间的差异小于某个阈值,则算法停止迭代,并返回分离后的信号和混合矩阵。