Multiscale dictionary learning for estimating conditional distributions

Published

Journal Article

Nonparametric estimation of the conditional distribution of a response given highdimensional features is a challenging problem. It is important to allow not only the mean but also the variance and shape of the response density to change flexibly with features, which are massive-dimensional. We propose a multiscale dictionary learning model, which expresses the conditional response density as a convex combination of dictionary densities, with the densities used and their weights dependent on the path through a tree decomposition of the feature space. A fast graph partitioning algorithm is applied to obtain the tree decomposition, with Bayesian methods then used to adaptively prune and average over different sub-trees in a soft probabilistic manner. The algorithm scales efficiently to approximately one million features. State of the art predictive performance is demonstrated for toy examples and two neuroscience applications including up to a million features.

Full Text

Duke Authors

Cited Authors

  • Petralia, F; Vogelstein, J; Dunson, DB

Published Date

  • January 1, 2013

Published In

International Standard Serial Number (ISSN)

  • 1049-5258

Citation Source

  • Scopus