Feature selection in sequential projection pursuit

A feature selection method is proposed to select a subset of variables in sequential projection pursuit (SPP) analysis in order to preserve as much sample clustering information as possible. The inhomogeneity of the complete data is explored by SPP, and the retained inhomogeneity information of a candidate subset is measured by means of the percentage of consensus in generalised procrustes analysis. The best subset is obtained by applying a genetic algorithm (GA) which optimises the consensus between the subset and the complete data set. An improved algorithm is proposed which enables analysis of high-dimensional data. The method was studied on three high-dimensional industrial data sets. The results show that the proposed method successfully identified inhomogeneity-bearing variables and leads to better subsets of variables than the other studied feature selection methods in preserving interesting clustering information.