Abstract
The treelet transform is a recent data reduction technique from the field of machine learning. Sharing many similarities with principal component analysis, the treelet transform can reduce a multidimensional dataset to the projections on a small number of directions or components that account for much of the variation in the original data. However, in contrast to principal component analysis, the treelet transform produces sparse components. This can greatly simplify interpretation. I describe the