Make a better plot for your PCA results FAST! Have your data points labeled, so you can see them and compare more easily!
Try this:
data = rand(30,20);
[coeff,~,~,~,explained] = pca(data);
h = pca_plot(coeff,explained,num2cell(1:20),[],'.');
Cite As
Jiun Yen (2026). qks1lver/pca_plot (https://github.com/qks1lver/pca_plot), GitHub. Retrieved .
MATLAB Release Compatibility
Created with
R2016a
Compatible with any release
Platform Compatibility
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- AI and Statistics > Statistics and Machine Learning Toolbox > Dimensionality Reduction and Feature Extraction >
Find more on Dimensionality Reduction and Feature Extraction in Help Center and MATLAB Answers
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Versions that use the GitHub default branch cannot be downloaded
| Version | Published | Release Notes | |
|---|---|---|---|
| 1.0.0.0 |
|
To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.
