你好我用MATLAB做PCA，下面的代码（我有13个属性）实际上我在运行程序（RBF网络）时遇到了问题，所以我用PCA来调整数据，我可以用这个方法吗？如果是，我应该使用矩阵 als 而不是我的真实数据吗？
% PCA1: Perform PCA using covariance.
% data - MxN matrix of input data
% (M dimensions, N trials)
% signals - MxN matrix of projected data
% PC - each column is a PC
% V - Mx1 matrix of variances
[M,N] = size(data);
% subtract off the mean for each dimension
mn = mean(data,2);
data = data - repmat(mn,1,N);
% calculate the covariance matrix
covariance = 1 / (N-1) * data * data’;
% find the eigenvectors and eigenvalues
[PC, V] = eig(covariance);
% extract diagonal of matrix as vector
V = diag(V);
% sort the variances in decreasing order
[junk, rindices] = sort(-1*V);
V = V(rindices);
PC = PC(:,rindices);
% project the original data set
sign
als = PC’ * data;
谢谢