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GPPCA

Code for Generalized Probabilistic Principal Component Analysis of Correlated Data

Mengyang Gu and Weining Shen (2019)

Journal of Machine Learning Research, In Press

arXiv preprint arXiv:1808.10868

This software is distributed under the terms of the GNU GENERAL PUBLIC LICENSE Version 2, April 2013.

This folder contains data and code reproduce the numerical results from the paper.

There are 5 folders.

src -- It contains the needed functions in C++ code to perform fast computing. In the code of R software, we use Rcpp and RcppEigen to call these functions. Demonstration -- The code to reproduce Figure 1 and Figure 2 in Example 1. Simulation -- The code to reproduce all the simulation results in Section 4. Real_example_1 -- The code and data to reproduce Table 5 and Figure 8 in Section 5.1 Real_example_2 -- The code and data to reproduce Table 6, Figure 8 and Figure 9 in Section 5.2

Mengyang Gu Department of Statistics and Applied Probability University of California, Santa Barbara

Email: mengyang@pstat.ucsb.edu

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Code for generalized probabilistic principal component analysis of correlated data

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