Send to

Choose Destination
See comment in PubMed Commons below
J Chem Inf Comput Sci. 2000 Sep-Oct;40(5):1160-8.

Unsupervised forward selection: a method for eliminating redundant variables.

Author information

  • 1Centre for Molecular Design, Institute of Biomedical and Biomolecular Science, University of Portsmouth, UK.


An unsupervised learning method is proposed for variable selection and its performance assessed using three typical QSAR data sets. The aims of this procedure are to generate a subset of descriptors from any given data set in which the resultant variables are relevant, redundancy is eliminated, and multicollinearity is reduced. Continuum regression, an algorithm encompassing ordinary least squares regression, regression on principal components, and partial least squares regression, was used to construct models from the selected variables. The variable selection routine is shown to produce simple, robust, and easily interpreted models for the chosen data sets.

[PubMed - indexed for MEDLINE]
PubMed Commons home

PubMed Commons

How to join PubMed Commons

    Supplemental Content

    Loading ...
    Write to the Help Desk