www.gusucode.com > 降维工具箱 - drtoolbox源码程序 > 降维工具箱 - drtoolbox\drtoolbox(降维工具箱)\techniques\pca.m
function [mappedX, mapping] = pca(X, no_dims) %PCA Perform the PCA algorithm % % [mappedX, mapping] = pca(X, no_dims) % % The function runs PCA on a set of datapoints X. The variable % no_dims sets the number of dimensions of the feature points in the % embedded feature space (no_dims >= 1, default = 2). % For no_dims, you can also specify a number between 0 and 1, determining % the amount of variance you want to retain in the PCA step. % The function returns the locations of the embedded trainingdata in % mappedX. Furthermore, it returns information on the mapping in mapping. % % % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.2b. % The toolbox can be obtained from http://www.cs.unimaas.nl/l.vandermaaten % You are free to use, change, or redistribute this code in any way you % want. However, it is appreciated if you maintain the name of the original % author. % % (C) Laurens van der Maaten % Maastricht University, 2007 if ~exist('no_dims', 'var') no_dims = 2; end % Make sure data is zero mean mapping.mean = mean(X, 1); X = X - repmat(mapping.mean, [size(X, 1) 1]); % Compute covariance matrix C = cov(X); % Perform eigendecomposition of C C(isnan(C)) = 0; C(isinf(C)) = 0; [M, lambda] = eig(C); % Sort eigenvectors in descending order [lambda, ind] = sort(diag(lambda), 'descend'); M = M(:,ind(1:no_dims)); % Apply mapping on the data mappedX = X * M; % Store information for out-of-sample extension mapping.M = M; mapping.lambda = lambda;