A review of unsupervised learning in astronomy
<|reference_start|>A review of unsupervised learning in astronomy: 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.<|reference_end|>
@article{fotopoulou2024a,
    title={A review of unsupervised learning in astronomy},
    author={Sotiria Fotopoulou (1) ((1) University of Bristol)},
    journal={arXiv preprint arXiv:2406.17316},
    year={2024},
    doi={10.1016/j.ascom.2024.100851},
    archivePrefix={arXiv},
    eprint={2406.17316},
    primaryClass={astro-ph.IM cs.LG}
}