Small size of observation and huge features happens a lot in shape/image and bioinformatics analysis. This file provides an alternative way of perform PCA analysis.
More detail about PCA please check: http://www.math.fsu.edu/~qxu/TCI.html
引用格式
Kim Xu (2024). Principal Component Analysis for large feature and small observation (https://www.mathworks.com/matlabcentral/fileexchange/45967-principal-component-analysis-for-large-feature-and-small-observation), MATLAB Central File Exchange. 检索时间: .
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R2009b
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- AI and Statistics > Statistics and Machine Learning Toolbox > Dimensionality Reduction and Feature Extraction >
在 Help Center 和 MATLAB Answers 中查找有关 Dimensionality Reduction and Feature Extraction 的更多信息
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启发作品: EOF
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