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PROCEDIA
2010
115views more  PROCEDIA 2010»
9 years 5 months ago
Generative topographic mapping by deterministic annealing
Generative Topographic Mapping (GTM) is an important technique for dimension reduction which has been successfully applied to many fields. However the usual Expectation-Maximizat...
Jong Youl Choi, Judy Qiu, Marlon E. Pierce, Geoffr...
NECO
1998
116views more  NECO 1998»
9 years 6 months ago
GTM: The Generative Topographic Mapping
Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. A familiar example ...
Christopher M. Bishop, Markus Svensén, Chri...
IJON
1998
102views more  IJON 1998»
9 years 6 months ago
Developments of the generative topographic mapping
The Generative Topographic Mapping (GTM) model was introduced by 7) as a probabilistic re-formulation of the self-organizing map (SOM). It offers a number of advantages compared ...
Christopher M. Bishop, Markus Svensén, Chri...
IJCSS
2000
98views more  IJCSS 2000»
9 years 6 months ago
The generative topographic mapping as a principal model for data visualization and market segmentation: an electronic commerce c
The process of extracting knowledge from data involves the discovery of patterns of interest which may be implicit, for instance, in speci
Alfredo Vellido, Paulo J. G. Lisboa, Karon Meehan
NPL
2000
95views more  NPL 2000»
9 years 6 months ago
Bayesian Sampling and Ensemble Learning in Generative Topographic Mapping
Generative topographic mapping (GTM) is a statistical model to extract a hidden smooth manifold from data, like the self-organizing map (SOM). Although a deterministic search algo...
Akio Utsugi
NN
2006
Springer
9 years 7 months ago
Missing data imputation through GTM as a mixture of t-distributions
The Generative Topographic Mapping (GTM) was originally conceived as a probabilistic alternative to the well-known, neural networkinspired, Self-Organizing Maps. The GTM can also ...
Alfredo Vellido
HPDC
2010
IEEE
9 years 8 months ago
Browsing large scale cheminformatics data with dimension reduction
Visualization of large-scale high dimensional data tool is highly valuable for scientific discovery in many fields. We present PubChemBrowse, a customized visualization tool for c...
Jong Youl Choi, Seung-Hee Bae, Judy Qiu, Geoffrey ...
CCGRID
2010
IEEE
9 years 8 months ago
High Performance Dimension Reduction and Visualization for Large High-Dimensional Data Analysis
Abstract--Large high dimension datasets are of growing importance in many fields and it is important to be able to visualize them for understanding the results of data mining appro...
Jong Youl Choi, Seung-Hee Bae, Xiaohong Qiu, Geoff...
IDEAL
2005
Springer
10 years 16 days ago
Differential Priors for Elastic Nets
The elastic net and related algorithms, such as generative topographic mapping, are key methods for discretized dimension-reduction problems. At their heart are priors that specify...
Miguel Á. Carreira-Perpiñán, ...
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