ActUp: Analyzing and Consolidating tSNE & UMAP

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearch


tSNE and UMAP are popular dimensionality reduction algorithms due to their speed and interpretable low-dimensional embeddings. Despite their popularity, however, little work has been done to study their full span of differences. We theoretically and experimentally evaluate the space of parameters in both tSNE and UMAP and observe that a single one - the normalization - is responsible for switching between them. This, in turn, implies that a majority of the algorithmic differences can be toggled without affecting the embeddings. We discuss the implications this has on several theoretic claims behind UMAP, as well as how to reconcile them with existing tSNE interpretations. Based on our analysis, we provide a method (GDR) that combines previously incompatible techniques from tSNE and UMAP and can replicate the results of either algorithm. This allows our method to incorporate further improvements, such as an acceleration that obtains either method's outputs faster than UMAP. We release improved versions of tSNE, UMAP, and GDR that are fully plug-and-play with the traditional libraries.

Original languageEnglish
Title of host publicationProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23)
Number of pages8
PublisherInternational Joint Conferences on Artificial Intelligence
Publication dateAug 2023
Publication statusPublished - Aug 2023
Event32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 - Macao, China
Duration: 19 Aug 202325 Aug 2023


Conference32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
SponsorInternational Joint Conferences on Artifical Intelligence (IJCAI)
SeriesProceedings of the International Joint Conference on Artificial Intelligence


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