About: Nonlinear dimensionality reduction

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High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lies within lower-dimensional space. If the data of interest is of low enough dimension, the data can be visualised in the low-dimensional space.

Attributes	Values
rdf:type	Thing
rdfs:label	Nonlinear dimensionality reduction (en)
rdfs:comment	High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lies within lower-dimensional space. If the data of interest is of low enough dimension, the data can be visualised in the low-dimensional space. (en)
rdfs:seeAlso	Manifold regularization
sameAs	Nonlinear dimensionality reduction
dbp:wikiPageUsesTemplate	dbt:Main dbt:Reflist dbt:See_also dbt:Short_description dbt:Snd
Subject	Dimension reduction
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction?oldid=1068346274&ns=0
Wikipage page ID	309261 (xsd:integer)
page length (characters) of wiki page	48154 (xsd:nonNegativeInteger)
Wikipage revision ID	1068346274 (xsd:integer)
Link from a Wikipage to another Wikipage	Floyd–Warshall algorithm Fourier series Local tangent space alignment Locally connected space Nonlinear dimensionality reduction Projection (linear algebra) Projective space T-distributed stochastic neighbor embedding Autoencoder Backpropagation Radial basis function network Self-organizing map Semidefinite embedding Whitney embedding theorem Dimension reduction Generative topographic map Geodesic Global cascades model Diffeomorphism Diffusion Diffusion map Dimension Dimensionality reduction Distance (graph theory) Distance matrix Charles-Augustin de Coulomb Curse of dimensionality Degree matrix K-nearest neighbors algorithm Linear discriminant analysis Principal component analysis Sammon mapping Concentration of measure Coulomb's law Dynamical system Eigendecomposition of a matrix Elastic map Restricted Boltzmann machine Sparse matrix Spectral submanifold Sphere Gaussian process Graduated optimization Independent component analysis Kernel principal component analysis Swiss roll Factor analysis Feature learning Growing self-organizing map Hamming space Invariant manifold Kosambi–Karhunen–Loève theorem Manifold Manifold alignment Manifold hypothesis Trevor Hastie Euclidean distance Stress majorization Toroid Isomap Klein bottle Laplace operator Latent variable model Markov chain Multidimensional scaling Neural network Random walk Bergman kernel Riemannian manifold Similarity measure Singular value decomposition Takens's theorem dbr:Density_networks dbr:Curvilinear_component_analysis dbr:Gaussian_process_latent_variable_model dbr:Hessian_LLE dbr:Locally-Linear_Embedding dbr:NeuroScale dbr:Principal_curve dbr:Topologically_Constrained_Isometric_Embedding

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