Principal Component Analysis (PCA) in MATLAB软件

This is a demonstration of how one can use PCA to classify a 2D data set.

软件应用简介

This is a demonstration of how one can use PCA to classify a 2D data set. This is the simplest form of PCA but you can easily extend it to higher dimensions and you can do image classification with PCA

PCA consists of a number of steps: 

– Loading the data 

– Subtracting the mean of the data from the original dataset 

– Finding the covariance matrix of the dataset 

– Finding the eigenvector(s) associated with the greatest eigenvalue(s) 

– Projecting the original dataset on the eigenvector(s)

Note: MATLAB has a built-in PCA functions. This file shows how a PCA works

界面展示

结果示意

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