Principal Component Analysis (PCA) in MATLAB
by Siamak Faridani
01 Jun 2009
This is a demonstration of how one can use PCA to classify a 2D data set.
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| File Information |
| Description |
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)
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| Other requirements |
I wrote it on Matlab 7.5.0 |
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