使用PCA算法调整数据

Question

你好我用MATLAB做下列代码的PCA（我有13个属性）实际上当我运行程序（RBF网络）时有问题，所以我用PCA调整数据，我可以吗使用这种方法？如果是的话，我应该使用矩阵代替我的真实数据吗？

% 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; 

由于

Answer 1

是，矩阵als是新的变换的数据集。为了控制这些新数据的维度，您可以通过采用最重要的k向量来修改PC;

PC = PC(:,1:k); 

为了在寻找新的样品转化相当于X（1 N）你可以写：

X_transformed = PC’ * X;