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Probabilistic PCA and Factor Analysis

version 1.0.0.0 (5.13 KB) by Mo Chen
EM algorithm for fitting PCA and FA model. This is probabilistic treatment of dimensional reduction.

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Updated 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).

Comments and Ratings (1)

Kris Villez

Just tested the fa.m function. Works well but had to decrease the tolerance to 1e-8 to make have accuracy comparable to 'factoran' on some challenging data sets.

Updates

1.0.0.0

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1.0.0.0

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MATLAB Release Compatibility
Created with R2016a
Compatible with any release
Platform Compatibility
Windows macOS Linux
Acknowledgements

Inspired by: Pattern Recognition and Machine Learning Toolbox