使用PCA算法調整數據

Question

你好我用MATLAB做下列代碼的PCA（我有13個屬性）實際上當我運行程序（RBF網絡）時有問題，所以我用PCA調整數據，我可以嗎使用這種方法？如果是的話，我應該使用矩陣代替我的真實數據嗎？

% 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; 

由於

Answer 1

是，矩陣als是新的變換的數據集。爲了控制這些新數據的維度，您可以通過採用最重要的k向量來修改PC;

PC = PC(:,1:k); 

爲了在尋找新的樣品轉化相當於X（1 N）你可以寫：

X_transformed = PC’ * X;