Title:
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The effect of noise and sample size in the performance of an unsupervised feature relevant determination method for manifold learning
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Author:
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Velazco Brao, Jorge Sebastián
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Other authors:
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Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Vellido Alcacena, Alfredo |
Abstract:
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The research on unsupervised feature selection is scarce in comparison to that for supervised
models, despite the fact that this is an important issue for many clustering
problems. An unsupervised feature selection method for general Finite Mixture Models
was recently proposed and subsequently extended to Generative Topographic Mapping
(GTM), a manifold learning constrained mixture model that provides data clustering
and visualization. Some of the results of previous research on this unsupervised feature
selection method for GTM suggested that its performance may be affected by insuficient
sample size and by noisy data. In this thesis, we test in detail such limitations of the
method and outline some techniques that could provide an at least partial solution to
the negative effect of the presence of uninformative noise. In particular, we provide a
detailed account of a variational Bayesian formulation of feature relevance determination
for GTM. |
Subject(s):
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-Àrees temàtiques de la UPC::Ensenyament i aprenentatge::Innovació i Investigació educativa -Data mining -Pattern recognition systems -Mineria de dades -Reconeixement de formes (Informàtica) |
Rights:
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Attribution-NonCommercial-NoDerivs 2.5 Spain
http://creativecommons.org/licenses/by-nc-nd/2.5/es/ |
Document type:
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Research/Master Thesis |
Published by:
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Universitat Politècnica de Catalunya
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