文件名称：MATLAB实现主成分分析
文件大小：2KB
文件格式：M
更新时间：2018-10-10 10:35:31
降维，聚类
function [M,mappedX ] = 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. %