방법 비교

	자기 조직화 지도 (코호넨 지도)	국소 선형 임베딩 (LLE)	t-SNE
분야	머신러닝	머신러닝	머신러닝
계열	Machine learning	Machine learning	Machine learning
기원 연도	1982	2000	2008
창시자	Teuvo Kohonen	Sam Roweis & Lawrence Saul	van der Maaten, L. & Hinton, G.
유형	Unsupervised neural network for topology-preserving mapping	Nonlinear manifold dimensionality reduction	Nonlinear dimensionality reduction (manifold visualization)
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.	van der Maaten, L. & Hinton, G. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(86), 2579–2605.
별칭	SOM, Kohonen map, Kohonen network, öz-örgütlemeli harita	LLE, manifold learning, nonlinear dimensionality reduction, yerel doğrusal gömme	t-SNE (Boyut İndirgeme / Görselleştirme), t-distributed stochastic neighbor embedding, tsne
관련	3	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.	t-SNE (t-Distributed Stochastic Neighbor Embedding) is a nonlinear dimensionality-reduction method introduced by Laurens van der Maaten and Geoffrey Hinton in 2008 that maps high-dimensional data into a 2D or 3D space for visualization. It preserves probabilistic local similarities, so points that are neighbours in the original space stay close together, revealing cluster structure and local neighbourhoods.