Exploració per tema "Generative topographic mapping"
Ara es mostren els items 1-9 de 9
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Advances in clustering and visualization of time series using GTM through time
(2008-09)
Article
Accés restringit per política de l'editorialMost of the existing research on multivariate time series concerns supervised forecasting problems. In comparison, little research has been devoted to their exploration through unsupervised clustering and visualization. ... -
Capturing the dynamics of multivariate time series through visualization using generative topographic mapping through time
(2005-11)
Report de recerca
Accés obertMost of the existing research on time series concerns supervised forecasting problems. In comparison, little research has been devoted to unsupervised methods for the visual exploration of multivariate time series. In this ... -
Generative manifold learning for the exploration of partially labeled data
(Universitat Politècnica de Catalunya, 2009-10-01)
Tesi
Accés obertIn many real-world application problems, the availability of data labels for supervised learning is rather limited. Incompletely labeled datasets are common in many of the databases generated in some of the currently most ... -
Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developments
(2004-09)
Report de recerca
Accés obertThe Generative Topographic Mapping (GTM: Bishop et al. 1998a), a non-linear latent variable model, was originally defined as constrained mixture of Gaussians. Gaussian mixture models are known to lack robustness in the ... -
Making nonlinear manifold learning models interpretable: the manifold grand tour
(2015-12)
Article
Accés obertDimensionality reduction is required to produce visualisations of high dimensional data. In this framework, one of the most straightforward approaches to visualising high dimensional data is based on reducing complexity ... -
Manifold learning visualization of metabotropic glutamate receptors
(IOS Press, 2014)
Text en actes de congrés
Accés obertG-Protein-Coupled Receptors (GPCRs) are cell membrane proteins with a key role in biological processes. GPCRs of class C, in particular, are of great interest in pharmacology. The lack of knowledge about their 3-D structures ... -
Missing data imputation through generative topographic mapping as a mixture of t-distributions: Theoretical developments
(2004-11)
Report de recerca
Accés obertThe Generative Topographic Mapping (GTM) was originally conceived as a probabilistic alternative to the well-known, neural network-inspired, Self-Organizing Map (SOM). The GTM can also be interpreted as a constrained mixture ... -
Probability ridges and distortion flows: Visualizing multivariate time series using a variational Bayesian manifold learning method
(Springer, 2014)
Text en actes de congrés
Accés obertTime-dependent natural phenomena and artificial processes can often be quantitatively expressed as multivariate time series (MTS). As in any other process of knowledge extraction from data, the analyst can benefit from the ... -
Variational Bayesian generative topographic mapping
(2008-12)
Article
Accés restringit per política de l'editorialGeneral finite mixture models are powerful tools for the density-based grouping of multivariate i.i.d. data, but they lack data visualization capabilities, which reduces their practical applicability to real-world problems. ...