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  Exploring model selection techniques for nonlinear dimensionality reduction

Harmeling, S.(2007). Exploring model selection techniques for nonlinear dimensionality reduction (EDI-INF-RR-0960). Edinburgh, UK: School of Informatics, University of Edinburgh.

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EDI-INF-RR-0960.pdf (Publisher version), 4MB
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Harmeling, S1, Author              
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 Abstract: Nonlinear dimensionality reduction (NLDR) methods have become useful tools for practitioners who are faced with the analysis of high-dimensional data. Of course, not all NLDR methods are equally applicable to a particular dataset at hand. Thus it would be useful to come up with model selection criteria that help to choose among different NLDR algorithms. This paper explores various approaches to this problem and evaluates them on controlled data sets. Comprehensive experiments will show that model selection scores based on stability are not useful, while scores based on Gaussian processes are helpful for the NLDR problem.

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 Dates: 2007-03
 Publication Status: Published in print
 Pages: 20
 Publishing info: Edinburgh, UK : School of Informatics, University of Edinburgh
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 Identifiers: Report Nr.: EDI-INF-RR-0960
BibTex Citekey: 6346
 Degree: -

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