Low-dimensional embeddings of high-dimensional data

TL;DR

This paper reviews recent developments in creating low-dimensional embeddings of high-dimensional data, highlighting best practices, evaluating popular methods, and discussing ongoing challenges to guide future research and application.

Contribution

It provides a comprehensive, critical overview of recent embedding algorithms, offering guidance and identifying open problems in the field.

Findings

Evaluation of popular embedding methods on diverse datasets

Identification of best practices for creating embeddings

Discussion of remaining challenges and open problems

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.