Unsupervised forward selection: A method for eliminating redundant variables

An unsupervised learning method is proposed for variable selection and its performance assessed using three typical QSAR data sets; The aims of this p rocedure are to generate a subset Of descriptors from any given data set in which the resultant variables are relevant,redundancy is eliminated, and m ulticollinearity is reduced. Continuum regression, an algorithm encompassin g ordinary least squares regression, regression on principal components, an d partial least squares regression, was used to construct models from the s elected variables: The variable selection routine is shown to produce simpl e, robust, and easily interpreted models for the chosen data sets.