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.
Property | Value |
---|---|
dbo:abstract |
|
dbo:wikiPageExternalLink | |
dbo:wikiPageID |
|
dbo:wikiPageLength |
|
dbo:wikiPageRevisionID |
|
dbo:wikiPageWikiLink |
|
dbp:wikiPageUsesTemplate | |
dcterms:subject | |
gold:hypernym | |
rdf:type | |
rdfs:comment |
|
rdfs:label |
|
owl:sameAs | |
prov:wasDerivedFrom | |
foaf:isPrimaryTopicOf | |
is dbo:wikiPageDisambiguates of | |
is dbo:wikiPageRedirects of | |
is dbo:wikiPageWikiLink of | |
is foaf:primaryTopic of |