The code implements regular and multiscale parametric t-SNE in Pytorch.
Rename example.config.yaml to config.yaml before running the code.
The demosntration is in the Demonstration.ipynb notebook.
Also check out OpenTSNE.
References:
- L.J.P. van der Maaten and G.E. Hinton. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9(Nov):2579-2605, 2008.
- L.J.P. van der Maaten. Learning a Parametric Embedding by Preserving Local Structure. In Proceedings of the Twelfth International Conference on Artificial Intelligence & Statistics (AI-STATS), JMLR W&CP 5:384-391, 2009.
- de Bodt, Cyril and Mulders, Dounia and Verleysen, Michel a Lee, John. (2018). Perplexity-free t-SNE and twice Student tt-SNE.
- Francesco Crecchi, Cyril de Bodt, Michel Verleysen, John A. Lee and Davide Bacciu. Perplexity-free Parametric t-SNE. arXiv:2010.01359v1 [cs.LG] 3 Oct 2020