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Generative topographic map

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…

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Explore the main themes, entities and connections around Generative topographic map. Start with the topic map, then use the sections below for research and deeper semantic analysis.

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Details of the algorithm

Density networks Importance sampling Multi-layer perceptron Latent variable model Gaussian noise

9 related topics

Uses

Principal components analysis Deformational modelling

2 related topics

Comparison with Kohonen's self-organizing maps

1 related topics

Overview

Machine learning Self-organizing map Neighborhood Generative model Expectation–maximization (EM) algorithm

6 related topics

Topics to explore

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Overview

Machine learning
Self-organizing map
Neighborhood Neighbourhood (mathematics)
Generative model
Expectation–maximization (EM) algorithm Expectation–maximization algorithm
Christopher Bishop

Details of the algorithm

Density networks Density networks?action=edit&redlink=1
Importance sampling
Multi-layer perceptron
Latent variable model
Gaussian noise
Mixture of Gaussians Gaussian mixture model
Radial basis function network
Feature space
Linear transform

Uses

Principal components analysis
Deformational modelling Deformational modelling?action=edit&redlink=1

Comparison with Kohonen's self-organizing maps

PhD

Advanced semantic analysis

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Map overview Semantic statistics

Generative topographic map

Nodes23

Edges22

Triples7

Avg. degree1.91

Density0.086957

Components1

How this topic connects Entity context

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Generative topographic map

Top relations

related to External links · 7

Generative topographic map → Aston University, Bishop, Matlab, Neural Computing Research Group, Svensen, UK, Williams Generative Topographic Mapping

Important terminology Word statistics

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

data space latent model gtm nonlinear map noise som generative self-organizing algorithm mapping svensen approach deformation network example function em

Entity relationships Subject–Predicate–Object triples

Subject	Predicate	Object	Confidence	Src
Generative topographic map	related to External links	Bishop	0.60	section
Generative topographic map	related to External links	Svensen	0.60	section
Generative topographic map	related to External links	Williams Generative Topographic Mapping	0.60	section
Generative topographic map	related to External links	Neural Computing Research Group	0.60	section
Generative topographic map	related to External links	Aston University	0.60	section
Generative topographic map	related to External links	UK	0.60	section
Generative topographic map	related to External links	Matlab	0.60	section

Related concept clusters Concept neighborhoods

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Connections between topic areas Semantic bridges

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Min side: 3

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