A New Hybrid Approach for Unsupervised Gene Selection

In recent years, unsupervised gene (feature) selection has become an integral part of microarray analysis because of the large number of genes and complexity in biological systems. Principal components analysis (PCA) is one of the approaches which has been applied, even though principal components (PCs) have no clear physical meanings. In this paper, we present a PCA based feature selection within a wrapper framework called PFSBEM (hybrid PCA based feature selection and boost-expectation-maximization clustering). PFSBEM uses a two-step approach to select features. The first step retrieves feature subsets with original physical meaning based on their capacities to reproduce sample projections on PCs. The second step then searches for the best feature subsets that maximize clustering performance. Experiment results clearly show that our feature sets improve the class prediction with respect to the chosen performance criteria