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Principal Component Analysis (PCA) in MATLAB

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

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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

MATLAB release MATLAB 7.5 (R2007b)
Other requirements I wrote it on Matlab 7.5.0
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Comments and Ratings (5)
28 Nov 2013 Anuranjit  
06 Sep 2011 Adams Black

Good! but can i use it for image classfication pls?

06 Sep 2011 Adams Black  
13 Jul 2011 zhu zhu

thanks!

08 Feb 2011 Tobin

Thanks! This helped me to understand what PCA actually is about

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