3 research outputs found
The effect of noise and sample size on an unsupervised feature selection method for manifold learning
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.Postprint (published version
The effect of noise and sample size in the performance of an unsupervised feature relevant determination method for manifold learning
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
The effect of noise and sample size on an unsupervised feature selection method for manifold learning
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