Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE
<|reference_start|>Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE: This paper shows that the dimensionality reduction methods, UMAP and t-SNE, can be approximately recast as MAP inference methods corresponding to a generalized Wishart-based model introduced in ProbDR. This interpretation offers deeper theoretical insights into these algorithms, while introducing tools with which similar dimensionality reduction methods can be studied.<|reference_end|>
@article{ravuri2024towards,
    title={Towards One Model for Classical Dimensionality Reduction: A
  Probabilistic Perspective on UMAP and t-SNE},
    author={Aditya Ravuri, Neil D. Lawrence},
    journal={arXiv preprint arXiv:2405.17412},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17412},
    primaryClass={stat.ML cs.AI cs.LG}
}