PCA for dimension reduction in 1D data

Version 1.0.0 (2.05 KB) by Selva
using principal component analysis for dimension reduction of feature vector in the SVM classification problem
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Updated 28 Sep 2018

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PCA is used for projecting data matrix from higher dimension to lower dimension

Cite As

Selva (2024). PCA for dimension reduction in 1D data (https://www.mathworks.com/matlabcentral/fileexchange/68942-pca-for-dimension-reduction-in-1d-data), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2018b
Compatible with any release
Platform Compatibility
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Find more on Dimensionality Reduction and Feature Extraction in Help Center and MATLAB Answers

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Version Published Release Notes
1.0.0