השוואת שיטות

	מפת ארגון עצמי (מפת קוהונן)	Locally Linear Embedding (LLE)
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	1982	2000
הוגה השיטה	Teuvo Kohonen	Sam Roweis & Lawrence Saul
סוג	Unsupervised neural network for topology-preserving mapping	Nonlinear manifold dimensionality reduction
מקור מכונן	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.
כינויים	SOM, Kohonen map, Kohonen network, öz-örgütlemeli harita	LLE, manifold learning, nonlinear dimensionality reduction, yerel doğrusal gömme
קשורות	3	3
תקציר	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.