# A review of unsupervised learning in astronomy

arXiv: https://arxiv.org/abs/2406.17316 · HTML: https://mlsanity.com/paper/2406.17316/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 319 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 30 of 44)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring](https://mlsanity.com/paper/1911.09785/) (2019)
- [A Brief Review of Domain Adaptation](https://mlsanity.com/paper/2010.03978/) (2020)
- [Manifold learning: what, how, and why](https://mlsanity.com/paper/2311.03757/) (2023)
- [Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders](https://mlsanity.com/paper/1703.02435/) (2017)
- [Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning](https://mlsanity.com/paper/1905.07424/) (2019)
- [Self-Supervised Representation Learning for Astronomical Images](https://mlsanity.com/paper/2012.13083/) (2020)
- [Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy](https://mlsanity.com/paper/2211.03796/) (2022)
- [Practical Galaxy Morphology Tools from Deep Supervised Representation Learning](https://mlsanity.com/paper/2110.12735/) (2021)
- [A hybrid supervised/unsupervised machine learning approach to solar flare prediction](https://mlsanity.com/paper/1706.07103/) (2017)
- [Self-supervised similarity search for large scientific datasets](https://mlsanity.com/paper/2110.13151/) (2021)
- [Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology](https://mlsanity.com/paper/2310.05227/) (2023)
- [Radio Galaxy Zoo: Using semi-supervised learning to leverage large unlabelled data-sets for radio galaxy classification under data-set shift](https://mlsanity.com/paper/2204.08816/) (2022)
- [DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection](https://mlsanity.com/paper/2302.02005/) (2023)
- [Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders](https://mlsanity.com/paper/2107.12698/) (2021)
- [SKYNET: an efficient and robust neural network training tool for machine learning in astronomy](https://mlsanity.com/paper/1309.0790/) (2013)
- [Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO](https://mlsanity.com/paper/1706.07446/) (2017)
- [Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Survey](https://mlsanity.com/paper/1812.02183/) (2018)
- [A brief review of contrastive learning applied to astrophysics](https://mlsanity.com/paper/2306.05528/) (2023)
- [Radio Galaxy Zoo: Unsupervised Clustering of Convolutionally Auto-encoded Radio-astronomical Images](https://mlsanity.com/paper/1906.02864/) (2019)
- [Unsupervised Learning Architecture for Classifying the Transient Noise of Interferometric Gravitational-wave Detectors](https://mlsanity.com/paper/2111.10053/) (2021)
- [Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases](https://mlsanity.com/paper/1509.07823/) (2015)

## Built on by (most cited first, 2 of 2)

- [Cost-Sensitive Unbiased Risk Estimation for Multi-Class Positive-Unlabeled Learning](https://mlsanity.com/paper/2510.25226/) (2025)
- [Criteria-first, semantics-later: reproducible structure discovery in image-based sciences](https://mlsanity.com/paper/2602.15712/) (2026)

## Abstract (arXiv)

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.