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	Rzutowanie losowe	Lokalnie Liniowe Osadzanie (LLE)
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	1984	2000
Twórca	Johnson & Lindenstrauss (lemma); Achlioptas (sparse variant)	Sam Roweis & Lawrence Saul
Typ	Linear, data-oblivious dimensionality reduction	Nonlinear manifold dimensionality reduction
Źródło pierwotne	Johnson, W. B., & Lindenstrauss, J. (1984). Extensions of Lipschitz mappings into a Hilbert space. Contemporary Mathematics, 26, 189–206.	Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326.
Inne nazwy	random projections, Johnson-Lindenstrauss projection, sparse random projection, rastgele izdüşüm	LLE, manifold learning, nonlinear dimensionality reduction, yerel doğrusal gömme
Pokrewne	2	3
Podsumowanie	Random projection reduces dimensionality by multiplying the data by a random matrix, relying on the Johnson-Lindenstrauss lemma (1984), which guarantees that projecting onto enough random directions approximately preserves all pairwise distances. Unlike PCA it does not analyze the data at all — the projection is random and data-oblivious — making it extremely cheap and well suited to very high-dimensional data and streaming or privacy-sensitive settings.	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.