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A variational formulation for GTM through time
Olier Caparroso, Iván; Vellido Alcacena, Alfredo
Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. SOCO - Soft Computing
Generative Topographic Mapping (GTM) is a latent variable model that, in its original version, was conceived to provide clustering and visualization of multivariate, realvalued, i.i.d. data. It was also extended to deal with noni-i.i.d. data such as multivariate time series in a variant called GTM Through Time (GTM-TT), defined as a constrained Hidden Markov Model (HMM). In this paper, we provide the theoretical foundations of the reformulation of GTM-TT within the Variational Bayesian framework and provide an illustrative example of its application. This approach handles the presence of noise in the time series, helping to avert the problem of data overfitting.
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Bayesian statistical decision theory
Information visualization
Bayes methods
Data visualisation
Hidden Markov models
Time series
Variational techniques
Estadística bayesiana
Visualització de la informació
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IEEE
         

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