Principal Component Analysis (PCA) in MATLAB

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

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