About: Diffusion map

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Diffusion maps is a dimensionality reduction or feature extraction algorithm introduced by Coifman and Lafon which 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), 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, dif

Attributes	Values
rdf:type	album
rdfs:label	Diffusion map (en)
rdfs:comment	Diffusion maps is a dimensionality reduction or feature extraction algorithm introduced by Coifman and Lafon which 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), 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, dif (en)
foaf:depiction
dcterms:subject	Machine learning algorithms
Wikipage page ID	34072669 (xsd:integer)
Wikipage revision ID	1084681471 (xsd:integer)
Link from a Wikipage to another Wikipage	Ronald Coifman Embedding Feature extraction Measure space Machine learning algorithms Laplacian matrix Facial recognition system Dimensionality reduction Fokker–Planck equation Principal component analysis Laplace operator Manifold Integral kernel Random walk Markov chain Nonlinear dimensionality reduction Spectral clustering Heat diffusion Complex networks
sameAs	Diffusion map Diffusion map Diffusion map
dbp:wikiPageUsesTemplate	dbt:Reflist
thumbnail	wiki-commons:Special:FilePath/Diffusion_map_of_a_torodial_helix.jpg?width=300
has abstract	Diffusion maps is a dimensionality reduction or feature extraction algorithm introduced by Coifman and Lafon which 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), 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 give a global description of the data-set. Compared with other methods, the diffusion map algorithm is robust to noise perturbation and computationally inexpensive. (en)
gold:hypernym	Reduction
prov:wasDerivedFrom	wikipedia-en:Diffusion_map?oldid=1084681471&ns=0
page length (characters) of wiki page	18972 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Diffusion_map
is Link from a Wikipage to another Wikipage of	Kernel density estimation Dimensionality reduction Nonlinear dimensionality reduction Outline of machine learning Diffusion maps
is Wikipage redirect of	Diffusion maps
is foaf:primaryTopic of	wikipedia-en:Diffusion_map

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