So sánh phương pháp

	Phân tích thành phần chính	UMAP
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2002	2018
Người khởi xướng	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	McInnes, L.; Healy, J.; Melville, J.
Loại	Unsupervised dimensionality reduction	Nonlinear manifold-learning dimension reduction
Công trình gốc	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	McInnes, L., Healy, J. & Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426.
Tên gọi khác	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	UMAP (Uniform Manifold Approximation and Projection), uniform manifold approximation and projection, manifold dimension reduction
Liên quan	3	5
Tóm tắt	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.	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.