Principal Component Analysis for large feature and small observation

Versione 1.1.0.0 (379 Byte) da Kim Xu

This file is PCA for large feature.

Segui

5.0

(1)

1,1K download

Aggiornato 18 lug 2014

Visualizza la licenza

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

Cita come

Kim Xu (2026). Principal Component Analysis for large feature and small observation (https://it.mathworks.com/matlabcentral/fileexchange/45967-principal-component-analysis-for-large-feature-and-small-observation), MATLAB Central File Exchange. Recuperato marzo 8, 2026.

Compatibilità della release di MATLAB

Creato con R2009b

Compatibile con qualsiasi release

Compatibilità della piattaforma

Windows macOS Linux

Categorie

AI and Statistics > Statistics and Machine Learning Toolbox > Dimensionality Reduction and Feature Extraction >

Scopri di più su Dimensionality Reduction and Feature Extraction in Help Center e MATLAB Answers

Tag Aggiungi tag

Riconoscimenti

Ispirato: EOF

Apri in una nuova scheda

Versione	Pubblicato	Note della release
1.1.0.0	18 lug 2014	Update reference	Scarica
1.0.0.0	21 mar 2014		Scarica