PCA (Principial Component Analysis)

Version 1.2.0.0 (1.48 KB) by Andreas
Principal Component Analysis Implementation of LindsaySmithPCA.pdf
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Updated 18 Mar 2010

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- 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)
- Use only a certain number of the eigenvector(s)
- Do back-project to the original basis vectors

Implementation of
http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf

"A tutorial on Principial Component Analysis"

Cite As

Andreas (2024). PCA (Principial Component Analysis) (https://www.mathworks.com/matlabcentral/fileexchange/26793-pca-principial-component-analysis), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2007b
Compatible with any release
Platform Compatibility
Windows macOS Linux
Categories
Find more on Dimensionality Reduction and Feature Extraction in Help Center and MATLAB Answers
Acknowledgements

Inspired: EOF

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