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A variational formulation for GTM through time

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10.1109/IJCNN.2008.4633841
 
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hdl:2117/13370

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Olier Caparroso, Iván
Vellido Alcacena, AlfredoMés informacióMés informacióMés informació
Document typeConference report
Defense date2008
PublisherIEEE
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
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.
CitationOlier, I.; Vellido, A. A variational formulation for GTM through time. A: IEEE World Congress on Computational Intelligence / International Joint-Conference on Artificial Neural Networks. "IEEE International Joint Conference on Neural Networks 2008". IEEE, 2008, p. 517-522. 
URIhttp://hdl.handle.net/2117/13370
DOI10.1109/IJCNN.2008.4633841
ISBN978-3-540-68858-7
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  • Departament de Ciències de la Computació - Ponències/Comunicacions de congressos [1.218]
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