Probabilistic PCA and Factor Analysis

Versión 1.0.0.0 (5,13 KB) por Mo Chen
EM algorithm for fitting PCA and FA model. This is probabilistic treatment of dimensional reduction.
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Actualizado 13 mar 2016

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This package provides several functions that mainly use EM algorithm to fit probabilistic PCA and Factor analysis models.
PPCA is probabilistic counterpart of PCA model. PPCA has the advantage that it can be further extended to more advanced model, such as mixture of PPCA, Bayeisan PPCA or model dealing with missing data, etc. However, this package mainly served a research and teaching purpose for people to understand the model. The code is succinct so that it is easy to read and learn.
This package is now a part of the PRML toolbox (http://cn.mathworks.com/help/stats/ppca.html).

Citar como

Mo Chen (2024). Probabilistic PCA and Factor Analysis (https://www.mathworks.com/matlabcentral/fileexchange/55883-probabilistic-pca-and-factor-analysis), MATLAB Central File Exchange. Recuperado .

Compatibilidad con la versión de MATLAB
Se creó con R2016a
Compatible con cualquier versión
Compatibilidad con las plataformas
Windows macOS Linux
Categorías
Más información sobre Dimensionality Reduction and Feature Extraction en Help Center y MATLAB Answers.
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Inspirado por: Pattern Recognition and Machine Learning Toolbox

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1.0.0.0

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