About: Generative topographic map

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Generative topographic map (GTM) is a machine learning method that is a probabilistic counterpart of the self-organizing map (SOM), is probably convergent and does not require a shrinking neighborhood or a decreasing step size. It is a generative model: the data is assumed to arise by first probabilistically picking a point in a low-dimensional space, mapping the point to the observed high-dimensional input space (via a smooth function), then adding noise in that space. The parameters of the low-dimensional probability distribution, the smooth map and the noise are all learned from the training data using the expectation-maximization (EM) algorithm. GTM was introduced in 1996 in a paper by Christopher Bishop, Markus Svensen, and Christopher K. I. Williams.

Attributes	Values
rdf:type	software
rdfs:label	Generative topographic map (en)
rdfs:comment	Generative topographic map (GTM) is a machine learning method that is a probabilistic counterpart of the self-organizing map (SOM), is probably convergent and does not require a shrinking neighborhood or a decreasing step size. It is a generative model: the data is assumed to arise by first probabilistically picking a point in a low-dimensional space, mapping the point to the observed high-dimensional input space (via a smooth function), then adding noise in that space. The parameters of the low-dimensional probability distribution, the smooth map and the noise are all learned from the training data using the expectation-maximization (EM) algorithm. GTM was introduced in 1996 in a paper by Christopher Bishop, Markus Svensen, and Christopher K. I. Williams. (en)
sameAs	Generative topographic map Generative topographic map
dbp:wikiPageUsesTemplate	dbt:Empty_section
Subject	Artificial neural networks
gold:hypernym	Machine
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Generative_topographic_map?oldid=915248688&ns=0
Wikipage page ID	1091054 (xsd:integer)
page length (characters) of wiki page	5291 (xsd:nonNegativeInteger)
Wikipage revision ID	915248688 (xsd:integer)
Link from a Wikipage to another Wikipage	Machine learning Nonlinear dimensionality reduction Artificial neural network Radial basis function network Self-organizing map Artificial neural networks Generative model Doctor of Philosophy Chris Bishop Linear map Principal component analysis Connectionism Data mining Gaussian noise Importance sampling Mixture model Expectation–maximization algorithm Feature (machine learning) Pattern recognition Latent variable model Multilayer perceptron Neighbourhood (mathematics) Neural network software dbr:Deformational_modelling dbr:Density_networks
has abstract	Generative topographic map (GTM) is a machine learning method that is a probabilistic counterpart of the self-organizing map (SOM), is probably convergent and does not require a shrinking neighborhood or a decreasing step size. It is a generative model: the data is assumed to arise by first probabilistically picking a point in a low-dimensional space, mapping the point to the observed high-dimensional input space (via a smooth function), then adding noise in that space. The parameters of the low-dimensional probability distribution, the smooth map and the noise are all learned from the training data using the expectation-maximization (EM) algorithm. GTM was introduced in 1996 in a paper by Christopher Bishop, Markus Svensen, and Christopher K. I. Williams. (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Generative_topographic_map
is Wikipage redirect of	Generative Topographic Map Generative Topographic Mapping Generative topographic mapping
is Link from a Wikipage to another Wikipage of	Nonlinear dimensionality reduction Generative Topographic Map GTM Generative Topographic Mapping Generative topographic mapping
is foaf:primaryTopic of	http://en.wikipedia.org/wiki/Generative_topographic_map

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