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Exploring model selection techniques for nonlinear dimensionality reduction

2007

Technical Report

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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.

Author(s): Harmeling, S.
Number (issue): EDI-INF-RR-0960
Year: 2007
Month: March
Day: 0

Department(s): Empirical Inference
Bibtex Type: Technical Report (techreport)

Institution: School of Informatics, University of Edinburgh

Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@techreport{6346,
  title = {Exploring model selection techniques for nonlinear dimensionality reduction},
  author = {Harmeling, S.},
  number = {EDI-INF-RR-0960},
  organization = {Max-Planck-Gesellschaft},
  institution = {School of Informatics, University of Edinburgh},
  school = {Biologische Kybernetik},
  month = mar,
  year = {2007},
  month_numeric = {3}
}