  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.
