8b5040a8a5baf3e0e67386c2e3a9b903-Reviews.html

Summary: This paper addresses the problem of conditional density estimation with a high dimensional input space (p n), an important problems as most (if not all) current models for nonparametric conditional density estimation do not scale to high-dimensions. Moreover, datasets with high dimensional inputs but relatively small sample sizes are becoming increasingly common. The model for the conditional density f(y x) is defined in three stages. First, a tree structure is defined over the input space. Second, given the tree structure, C_{j,k}, the k th partition of the X space at scale j, is mapped to a lower dimensional space.