Principal Component Analysis (PCA) in MATLAB
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
引用格式
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. 检索来源 .
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- AI and Statistics > Statistics and Machine Learning Toolbox > Dimensionality Reduction and Feature Extraction >
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