Low-dimensional embeddings of high-dimensional data
This review addresses the lack of clear guidance for researchers and practitioners across domains like biology and humanities who need effective embedding methods for data analysis.
The paper tackles the challenge of navigating the fragmented field of low-dimensional embedding algorithms for high-dimensional data by providing a critical review, best practices, and evaluations on various datasets to guide practitioners.
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.