A review of unsupervised learning in astronomy

TL;DR

This review discusses the evolution and application of unsupervised learning methods in astronomy, highlighting traditional techniques, recent complex frameworks, and emerging semi-supervised approaches for data analysis.

Contribution

It provides a comprehensive overview of unsupervised learning methods in astronomy, including recent advances and future directions.

Findings

Dimensionality reduction techniques like PCA and auto-encoders are widely used.

Clustering methods such as k-means and HDBSCAN are fundamental for identifying similar groups.

Emerging self-supervised and semi-supervised methods combine supervised and unsupervised learning benefits.

Abstract

This review summarizes popular unsupervised learning methods, and gives an overview of their past, current, and future uses in astronomy. Unsupervised learning aims to organise the information content of a dataset, in such a way that knowledge can be extracted. Traditionally this has been achieved through dimensionality reduction techniques that aid the ranking of a dataset, for example through principal component analysis or by using auto-encoders, or simpler visualisation of a high dimensional space, for example through the use of a self organising map. Other desirable properties of unsupervised learning include the identification of clusters, i.e. groups of similar objects, which has traditionally been achieved by the k-means algorithm and more recently through density-based clustering such as HDBSCAN. More recently, complex frameworks have emerged, that chain together dimensionality reduction and clustering methods. However, no dataset is fully unknown. Thus, nowadays a lot of research has been directed towards self-supervised and semi-supervised methods that stand to gain from both supervised and unsupervised learning.