Principal Component Analysis (PCA) in MATLAB

This is a demonstration of how one can use PCA to classify a 2D data set.
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Aktualisiert 1. Jun 2009

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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

Zitieren als

Siamak Faridani (2024). Principal Component Analysis (PCA) in MATLAB (https://www.mathworks.com/matlabcentral/fileexchange/24322-principal-component-analysis-pca-in-matlab), MATLAB Central File Exchange. Abgerufen .

Kompatibilität der MATLAB-Version
Erstellt mit R2007b
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Version Veröffentlicht Versionshinweise
1.0.0.0