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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 nonorthogonal 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. Contact: joe.faith@unn.ac.uk

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