방법 비교

	자기 조직화 지도 (코호넨 지도)	국소 선형 임베딩 (LLE)
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1982	2000
창시자	Teuvo Kohonen	Sam Roweis & Lawrence Saul
유형	Unsupervised neural network for topology-preserving mapping	Nonlinear manifold dimensionality reduction
원전	Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59–69.	Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326.
별칭	SOM, Kohonen map, Kohonen network, öz-örgütlemeli harita	LLE, manifold learning, nonlinear dimensionality reduction, yerel doğrusal gömme
관련	3	3
요약	A self-organizing map is an unsupervised neural network, introduced by Teuvo Kohonen in 1982, that projects high-dimensional data onto a low-dimensional (usually two-dimensional) grid of prototype vectors while preserving the data's topology — nearby inputs map to nearby grid cells. It is used for visualization, clustering, and exploratory analysis, turning complex data into an ordered, interpretable map.	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.