📄 pca.m
字号:
function [patterns, targets, UW, m] = PCA(patterns, targets, dimension)
%Reshape the data points using the principal component analysis
%Inputs:
% train_patterns - Input patterns
% train_targets - Input targets
% dimension - Number of dimensions for the output data points
%
%Outputs
% patterns - New patterns
% targets - New targets
% UW - Reshape martix
% m - Original pattern averages
[r,c] = size(patterns);
if (r < dimension),
disp('Required dimension is larger than the data dimension.')
disp(['Will use dimension ' num2str(r)])
dimension = r;
end
%Calculate cov matrix and the PCA matrixes
m = mean(patterns')';
S = ((patterns - m*ones(1,c)) * (patterns - m*ones(1,c))');
[V, D] = eig(S);
W = V(:,r-dimension+1:r)';
U = S*W'*inv(W*S*W');
%Calculate new patterns
UW = U*W;
patterns = W*patterns;
⌨️ 快捷键说明
复制代码
Ctrl + C
搜索代码
Ctrl + F
全屏模式
F11
切换主题
Ctrl + Shift + D
显示快捷键
?
增大字号
Ctrl + =
减小字号
Ctrl + -