  Learning a distribution conditional on a set of discrete-valued features is a
commonly encountered task. This becomes more challenging with a
high-dimensional feature set when there is the possibility of interaction
between the features. In addition, many frequently applied techniques consider
only prediction of the mean, but the complete conditional density is needed to
answer more complex questions. We demonstrate a novel nonparametric Bayes
method based upon a tensor factorization of feature-dependent weights for
Gaussian kernels. The method makes use of multistage feature selection for
dimension reduction. The resulting conditional density morphs flexibly with the
selected features.
