So sánh phương pháp

	Isomap	Máy Vectơ Hỗ trợ (Phân loại)
Lĩnh vực	Học máy	Học máy
Họ	Latent structure	Machine learning
Năm ra đời	2000	1995
Người khởi xướng	Tenenbaum, J. B.; de Silva, V.; Langford, J. C.	Cortes, C. & Vapnik, V.
Loại	Manifold learning / nonlinear dimensionality reduction	Maximum-margin classifier (kernel method)
Công trình gốc	Tenenbaum, J. B., de Silva, V. & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319–2323.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Tên gọi khác	Isomap, isometric feature mapping, geodesic Isomap, nonlinear MDS	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Liên quan	3	5
Tóm tắt	Isomap (Isometric Feature Mapping) is a manifold learning algorithm introduced by Tenenbaum, de Silva, and Langford in 2000 that discovers the intrinsic low-dimensional geometry of high-dimensional data by preserving geodesic — rather than straight-line Euclidean — distances between all pairs of points. It was one of the earliest, and most influential, nonlinear dimensionality reduction methods to demonstrate that genuinely curved data manifolds could be unfolded into a faithful low-dimensional coordinate system.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.