Порівняння методів

	UMAP	Метод головних компонент
Галузь	Машинне навчання	Машинне навчання
Родина	Machine learning	Machine learning
Рік появи	2018	2002
Автор методу	McInnes, L.; Healy, J.; Melville, J.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Тип	Nonlinear manifold-learning dimension reduction	Unsupervised dimensionality reduction
Основоположне джерело	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.
Інші назви	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
Пов'язані	5	3
Підсумок	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.