Nonlinear statistical learning with truncated Gaussian graphical models

Published

Conference Paper

© 2016 by the author(s). We introduce the truncated Gaussian graphical model (TGGM) as a novel framework for designing statistical models for nonlinear learning. A TGGM is a Gaussian graphical model (GGM) with a subset of variables truncated to be nonneg- Ative. The truncated variables are assumed latent and integrated out to induce a marginal model. We show that the variables in the marginal model arc non-Gaussian distributed and their expected relations are nonlinear. We use expectation- maximization to break the inference of the nonlinear model into a sequence of TGGM inference problems, each of which is efficiently solved by using the properties and numerical methods of multivariate Gaussian distributions. We use the TGGM to design models for nonlinear regression and classification, with the performances of these models demonstrated on extensive benchmark datasets and compared to state-of-the-art competing results.

Duke Authors

Cited Authors

  • Su, Q; Liao, X; Chen, C; Carin, L

Published Date

  • January 1, 2016

Published In

  • 33rd International Conference on Machine Learning, Icml 2016

Volume / Issue

  • 4 /

Start / End Page

  • 2884 - 2895

International Standard Book Number 13 (ISBN-13)

  • 9781510829008

Citation Source

  • Scopus