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| Self-Organizing Map (Kohonen Map)× | Lokalt Lineær Indlejring (LLE)× | |
|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 1982 | 2000 |
| Ophavsperson≠ | Teuvo Kohonen | Sam Roweis & Lawrence Saul |
| Type≠ | Unsupervised neural network for topology-preserving mapping | Nonlinear manifold dimensionality reduction |
| Oprindelig kilde≠ | Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59–69. DOI ↗ | Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326. DOI ↗ |
| Aliasser | SOM, Kohonen map, Kohonen network, öz-örgütlemeli harita | LLE, manifold learning, nonlinear dimensionality reduction, yerel doğrusal gömme |
| Relaterede | 3 | 3 |
| Resumé≠ | 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. |
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