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Statements

Subject Item

dbr:Generative_topographic_map

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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.

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dbc:Artificial_neural_networks

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dbr:Machine

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n12:Generative_topographic_map?oldid=915248688&ns=0

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dbr:Linear_map dbr:Pattern_recognition dbr:Multilayer_perceptron dbr:Principal_component_analysis dbr:Machine_learning dbc:Artificial_neural_networks dbr:Expectation–maximization_algorithm dbr:Radial_basis_function_network dbr:Christopher_Bishop dbr:Artificial_neural_network dbr:Neural_network_software dbr:Nonlinear_dimensionality_reduction dbr:Self-organizing_map dbr:Gaussian_noise dbr:Neighbourhood_(mathematics) dbr:Mixture_model dbr:Connectionism dbr:Doctor_of_Philosophy dbr:Deformational_modelling dbr:Latent_variable_model dbr:Importance_sampling dbr:Density_networks dbr:Feature_(machine_learning) dbr:Generative_model dbr:Data_mining

dbo: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.

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n12:Generative_topographic_map