Dimensionality Reduction: Foundations and Applications in Clinical Neuroscience.
Journal
Acta neurochirurgica. Supplement
ISSN: 0065-1419
Titre abrégé: Acta Neurochir Suppl
Pays: Austria
ID NLM: 100962752
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
4
12
2021
pubmed:
5
12
2021
medline:
15
12
2021
Statut:
ppublish
Résumé
Advancements in population neuroscience are spurred by the availability of large scale, open datasets, such as the Human Connectome Project or recently introduced UK Biobank. With the increasing data availability, analyses of brain imaging data employ more and more sophisticated machine learning algorithms. However, all machine learning algorithms must balance generalization and complexity. As the detail of neuroimaging data leads to high-dimensional data spaces, model complexity and hence the chance of overfitting increases. Different methodological approaches can be applied to alleviate the problems that arise in high-dimensional settings by reducing the original information into meaningful and concise features. One popular approach is dimensionality reduction, which allows to summarize high-dimensional data into low-dimensional representations while retaining relevant trends and patterns. In this paper, principal component analysis (PCA) is discussed as widely used dimensionality reduction method based on current examples of population-based neuroimaging analyses.
Identifiants
pubmed: 34862528
doi: 10.1007/978-3-030-85292-4_8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
59-63Informations de copyright
© 2022. The Author(s), under exclusive license to Springer Nature Switzerland AG.
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