Сравнение методов

	UMAP	Анализ главных компонент
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2018	2002
Автор метода	McInnes, L.; Healy, J.; Melville, J.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Тип	Nonlinear manifold-learning dimension reduction	Unsupervised dimensionality reduction
Основополагающий источник	McInnes, L., Healy, J. & Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Другие названия	UMAP (Uniform Manifold Approximation and Projection), uniform manifold approximation and projection, manifold dimension reduction	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Связанные	5	3
Сводка	UMAP (Uniform Manifold Approximation and Projection) is a fast, scalable nonlinear dimension-reduction method grounded in manifold-learning theory, introduced by McInnes, Healy and Melville in 2018. It compresses high-dimensional data into a low-dimensional embedding for visualisation and downstream analysis.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.