Editing Machine learning (section)

=== Dimensionality reduction ===
[[Dimensionality reduction]] is a process of reducing the number of random variables under consideration by obtaining a set of principal variables.<ref>{{cite journal|url=https://science.sciencemag.org/content/290/5500/2323|title=Nonlinear Dimensionality Reduction by Locally Linear Embedding|first1=Sam T.|last1=Roweis|first2=Lawrence K.|last2=Saul|date=22 December 2000|journal=Science|volume=290|issue=5500|pages=2323–2326|doi=10.1126/science.290.5500.2323|pmid=11125150|bibcode=2000Sci...290.2323R|s2cid=5987139|language=en|access-date=17 July 2023|archive-date=15 August 2021|archive-url=https://web.archive.org/web/20210815021528/https://science.sciencemag.org/content/290/5500/2323|url-status=live|url-access=subscription}}</ref> In other words, it is a process of reducing the dimension of the [[Feature (machine learning)|feature]] set, also called the "number of features". Most of the dimensionality reduction techniques can be considered as either feature elimination or [[Feature extraction|extraction]]. One of the popular methods of dimensionality reduction is [[principal component analysis]] (PCA). PCA involves changing higher-dimensional data (e.g., 3D) to a smaller space (e.g., 2D).
The [[manifold hypothesis]] proposes that high-dimensional data sets lie along low-dimensional [[manifold]]s, and many dimensionality reduction techniques make this assumption, leading to the area of [[manifold learning]] and [[manifold regularisation]].