विधियों की तुलना करें

	स्व-संगठित मानचित्र (कोहोनन मानचित्र)	स्थानीय रूप से रैखिक एम्बेडिंग (Locally Linear Embedding (LLE))	टी-एसएनई
क्षेत्र	मशीन अधिगम	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning	Machine learning
उद्भव वर्ष	1982	2000	2008
प्रवर्तक	Teuvo Kohonen	Sam Roweis & Lawrence Saul	van der Maaten, L. & Hinton, G.
प्रकार	Unsupervised neural network for topology-preserving mapping	Nonlinear manifold dimensionality reduction	Nonlinear dimensionality reduction (manifold visualization)
मौलिक स्रोत	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.
उपनाम	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
संबंधित	3	3	3
सारांश	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.