VAE-SNE is a deep generative model for both dimensionality reduction and clustering. VAE-SNE is a variational autoencoder (VAE) regularized with the stochastic neighbor embedding (t-SNE/SNE) objective to improve local structure preservation in the compressed latent space. The model simultaneously learns a Gaussian mixture cluster distribution during optimization, and overlapping mixture components are then combined using a sparse watershed procedure, so the number of clusters does not have to be specified manually — provided the number of Gaussian mixture components is large enough. VAE-SNE produces embeddings with similar quality to existing dimensionality reduction methods; can detect outliers; scales to large, out-of-core datasets; and can easily add new data to an existing embedding/clustering.

The code and documentation for VAE-SNE are coming soon. For now you can read more about it in our preprint: Graving, Jacob M., and Couzin, Iain D. 2020. VAE-SNE: a deep generative model for simultaneous dimensionality reduction and clustering. https://doi.org/10.1101/2020.07.17.207993.

Released under a Apache 2.0 License. See LICENSE for details.