Title:
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A variational Bayesian formulation for GTM: Theoretical foundations
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Author:
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Olier Caparroso, Iván; Vellido Alcacena, Alfredo
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Other authors:
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Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. SOCO - Soft Computing |
Abstract:
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Generative Topographic Mapping (GTM) is a non-linear latent variable model of the manifold learning family that provides simultaneous visualization and clustering of high-dimensional data. It was originally formulated as a constrained mixture of Gaussian distributions, for which the adaptive parameters were determined by Maximum Likelihood (ML), using the Expectation-Maximization (EM) algorithm. In this paper, we define an alternative variational formulation of GTM that provides a full Bayesian treatment to a Gaussian Process (GP) - based variation of the model. |
Subject(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial -Generative models -Variational inference -Statistical machine learning |
Rights:
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Document type:
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Article - Published version Report |
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