Título:
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The effect of noise and sample size on an unsupervised feature selection method for manifold learning
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Autor/a:
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Vellido Alcacena, Alfredo; Velazco, Jorge
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Otros autores:
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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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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 visualization. Some of the results of a previous partial assessment of this unsupervised feature selection method
for GTM suggested that its performance may be affected by insufficient sample size and by noisy data. In this brief study, we test in some detail such limitations of the method. |
Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic -Machine learning -Information visualization -Data visualisation -Feature extraction -Pattern clustering -Sampling methods -Unsupervised learning -Aprenentatge automàtic -Visualització de la informació |
Derechos:
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Tipo de documento:
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Artículo - Versión publicada Objeto de conferencia |
Editor:
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IEEE
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Compartir:
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