The effect of noise and sample size in the performance of an unsupervised feature relevant determination method for manifold learning
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Cita com:
hdl:2099.1/5607
Tipus de documentProjecte Final de Màster Oficial
Data2008-09
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 2.5 Espanya
Abstract
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.
MatèriesData mining, Pattern recognition systems, Mineria de dades, Reconeixement de formes (Informàtica)
ProvinençaAquest document conté originàriament altre material i/o programari no inclòs en aquest lloc web
TitulacióMÀSTER UNIVERSITARI EN INTEL·LIGÈNCIA ARTIFICIAL (Pla 2009)
Col·leccions
Fitxers | Descripció | Mida | Format | Visualitza |
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ThesisJorgeVelazco.pdf | 4,992Mb | Visualitza/Obre |