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- Diffusion_map abstract "Diffusion maps is a dimensionality reduction or feature extraction algorithm introduced by R. R. Coifman and S. Lafon. It computes a family of embeddings of a data set into Euclidean space (often low-dimensional) whose coordinates can be computed from the eigenvectors and eigenvalues of a diffusion operator on the data. The Euclidean distance between points in the embedded space is equal to the "diffusion distance" between probability distributions centered at those points. Different from linear dimensionality reduction methods such as principal component analysis (PCA) and multi-dimensional scaling (MDS), diffusion maps is part of the family of nonlinear dimensionality reduction methods which focus on discovering the underlying manifold that the data has been sampled from. By integrating local similarities at different scales, diffusion maps gives a global description of the data-set. Compared with other methods, the diffusion maps algorithm is robust to noise perturbation and is computationally inexpensive.".
- Diffusion_map wikiPageID "34072669".
- Diffusion_map wikiPageLength "14647".
- Diffusion_map wikiPageOutDegree "15".
- Diffusion_map wikiPageRevisionID "680803560".
- Diffusion_map wikiPageWikiLink Category:Machine_learning_algorithms.
- Diffusion_map wikiPageWikiLink Dimensionality_reduction.
- Diffusion_map wikiPageWikiLink Feature_extraction.
- Diffusion_map wikiPageWikiLink Fokker-Planck_equation.
- Diffusion_map wikiPageWikiLink Fokker–Planck_equation.
- Diffusion_map wikiPageWikiLink Heat_diffusion.
- Diffusion_map wikiPageWikiLink Heat_equation.
- Diffusion_map wikiPageWikiLink Integral_kernel.
- Diffusion_map wikiPageWikiLink Integral_transform.
- Diffusion_map wikiPageWikiLink Laplace-Beltrami_operator.
- Diffusion_map wikiPageWikiLink Laplace–Beltrami_operator.
- Diffusion_map wikiPageWikiLink Manifold.
- Diffusion_map wikiPageWikiLink Markov_chain.
- Diffusion_map wikiPageWikiLink Multi-dimensional_scaling.
- Diffusion_map wikiPageWikiLink Multidimensional_scaling.
- Diffusion_map wikiPageWikiLink Nonlinear_dimensionality_reduction.
- Diffusion_map wikiPageWikiLink Principal_component_analysis.
- Diffusion_map wikiPageWikiLink Ronald_Coifman.
- Diffusion_map wikiPageWikiLinkText "Diffusion map".
- Diffusion_map wikiPageWikiLinkText "diffusion map".
- Diffusion_map hasPhotoCollection Diffusion_map.
- Diffusion_map wikiPageUsesTemplate Template:Reflist.
- Diffusion_map subject Category:Machine_learning_algorithms.
- Diffusion_map hypernym Reduction.
- Diffusion_map type Organisation.
- Diffusion_map type Algorithm.
- Diffusion_map comment "Diffusion maps is a dimensionality reduction or feature extraction algorithm introduced by R. R. Coifman and S. Lafon. It computes a family of embeddings of a data set into Euclidean space (often low-dimensional) whose coordinates can be computed from the eigenvectors and eigenvalues of a diffusion operator on the data. The Euclidean distance between points in the embedded space is equal to the "diffusion distance" between probability distributions centered at those points.".
- Diffusion_map label "Diffusion map".
- Diffusion_map sameAs m.0hrf6nb.
- Diffusion_map sameAs Q5275440.
- Diffusion_map sameAs Q5275440.
- Diffusion_map wasDerivedFrom Diffusion_map?oldid=680803560.
- Diffusion_map isPrimaryTopicOf Diffusion_map.