Exploratory basis pursuit classification

Feature selection is a fundamental process in many classifier design problems. However, it is NP-complete and approximate approaches often require requires extensive exploration and evaluation. This paper describes a novel approach that represents feature selection as a continuous regularization problem which has a single, global minimum, where the model s complexity is measured using a 1-norm on the parameter vector. A new exploratory design process is also described that allows the designer to efficiently construct the complete locus of sparse, kernel-based classifiers. It allows the designer to investigate the optimal parameters trajectories as the regularization parameter is altered and look for effects, such as Simpson s paradox, that occur in many multivariate data analysis problems. The approach is demonstrated on the well-known Australian Credit data set. 2005 Published by Elsevier B.V.