PCA (Principial Component Analysis)
Principal Component Analysis Implementation of LindsaySmithPCA.pdf
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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 (2026). PCA (Principial Component Analysis) (https://se.mathworks.com/matlabcentral/fileexchange/26793-pca-principial-component-analysis), MATLAB Central File Exchange. Retrieved .
Acknowledgements
Inspired: EOF
Categories
Find more on Dimensionality Reduction and Feature Extraction in Help Center and MATLAB Answers
General Information
- Version 1.2.0.0 (1.48 KB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
