방법 비교

	국소 선형 임베딩 (LLE)	서포트 벡터 머신 (분류)
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2000	1995
창시자	Sam Roweis & Lawrence Saul	Cortes, C. & Vapnik, V.
유형	Nonlinear manifold dimensionality reduction	Maximum-margin classifier (kernel method)
원전	Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
별칭	LLE, manifold learning, nonlinear dimensionality reduction, yerel doğrusal gömme	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
관련	3	5
요약	Locally linear embedding, introduced by Sam Roweis and Lawrence Saul in 2000, is a manifold-learning method for nonlinear dimensionality reduction. It assumes that although data may curve through a high-dimensional space, each point and its neighbours lie approximately on a flat patch. LLE captures each point as a weighted combination of its neighbours and then finds a low-dimensional layout that preserves those same local relationships, unrolling curved structure into a faithful low-dimensional map.	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.