Learning Densities Conditional on Many Interacting Features
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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.
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