This paper shows that dimensionality reduction methods such as UMAP and t-SNE, can be approximately recast as MAP inference methods corresponding to a model introduced in Ravuri et al. (2023), that describes the graph Laplacian (an estimate of the data precision matrix) using a Wishart distribution, with a mean given by a non-linear covariance function evaluated on the latents. This interpretation offers deeper theoretical and semantic insights into such algorithms, and forging a connection to Gaussian process latent variable models by showing that well-known kernels can be used to describe covariances implied by graph Laplacians. We also introduce tools with which similar dimensionality reduction methods can be studied.
View on arXiv@article{ravuri2025_2405.17412,
title={ Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE },
author={ Aditya Ravuri and Neil D. Lawrence },
journal={arXiv preprint arXiv:2405.17412},
year={ 2025 }
}