Targeted Projection Pursuit for Gene Expression Data Classification and Visualisation

Abstract

We present a novel method for finding low dimensional views of high dimensional data: Targeted Projection Pursuit. The method proceeds by finding projections of the data that best approximate a target view. Two versions of the method are introduced; one version based on Procrustes analysis and one based on a single layer perceptron. These versions are capable of finding orthogonal or non-orthogonal projections respectively. The method is quantitatively and qualitatively compared with other dimension reduction techniques. It is shown to find two-dimensional views that display the classification of cancers from gene expression data with a visual separation equal to, or better than, existing dimension reduction techniques.