Low-dimensional embeddings of high-dimensional data

• URL: http://arxiv.org/abs/2508.15929v1
• Date: Thu, 21 Aug 2025 19:23:15 GMT
• Title: Low-dimensional embeddings of high-dimensional data
• Authors: Cyril de Bodt, Alex Diaz-Papkovich, Michael Bleher, Kerstin Bunte, Corinna Coupette, Sebastian Damrich, Enrique Fita Sanmartin, Fred A. Hamprecht, Emőke-Ágnes Horvát, Dhruv Kohli, Smita Krishnaswamy, John A. Lee, Boudewijn P. F. Lelieveldt, Leland McInnes, Ian T. Nabney, Maximilian Noichl, Pavlin G. Poličar, Bastian Rieck, Guy Wolf, Gal Mishne, Dmitry Kobak,
• Abstract summary: Low-dimensional embedding algorithms create low-dimensional representations, or embeddings, for data visualization, exploration, and analysis.<n>Numerous embedding algorithms have been developed, and their usage has become widespread in research and industry.<n>This review provides a detailed and critical overview of recent developments, derive a list of best practices for creating and using low-dimensional embeddings, evaluate popular approaches on a variety of datasets, and discuss the remaining challenges and open problems in the field.
• Score: 35.01192808772252
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large collections of high-dimensional data have become nearly ubiquitous across many academic fields and application domains, ranging from biology to the humanities. Since working directly with high-dimensional data poses challenges, the demand for algorithms that create low-dimensional representations, or embeddings, for data visualization, exploration, and analysis is now greater than ever. In recent years, numerous embedding algorithms have been developed, and their usage has become widespread in research and industry. This surge of interest has resulted in a large and fragmented research field that faces technical challenges alongside fundamental debates, and it has left practitioners without clear guidance on how to effectively employ existing methods. Aiming to increase coherence and facilitate future work, in this review we provide a detailed and critical overview of recent developments, derive a list of best practices for creating and using low-dimensional embeddings, evaluate popular approaches on a variety of datasets, and discuss the remaining challenges and open problems in the field.

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