This repository contains all the code needed to replicate the experiments presented in the article "Learning GPLVM with arbitrary kernels using the unscented transformation", preprint avaible at arXiv. The code for the Unscented Bayesian GPLVM model is not neatly packaged into a Python package yet but can be readly imported and used. See any of the Jupyter notebooks for example usage.
As noted, part of the code was adapted from the GPFlow project.
See requirements.txt.
Each Jupyter notebook have two variables named dataset and save_or_load,
these variables control which dataset is being used and what the notebook should
do. They are located in the third cell of each notebook.
For the dimensionality reduction task we used the following datasets:
dataset value |
Dataset |
|---|---|
"oil flow" |
Three Phase Oil dataset |
"USPS digits" |
USPS Digits dataset |
For the free simulation we used the following datasets:
dataset value |
Dataset |
|---|---|
"passengers" |
International Airline Passengers |
On both notebooks, the save_or_load variable controls the following behavior:
save_or_load value |
Behaviour |
|---|---|
"save" |
Run experiments and save images, tables and latent space/predictions |
"rerun" |
Run experiments but don't save any data |