Nonlinear statistical learning with truncated Gaussian graphical models

Qinliang Su, Xuejun Liao, Changyou Chen, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

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.
Original languageEnglish (US)
Title of host publication33rd International Conference on Machine Learning, ICML 2016
PublisherInternational Machine Learning Society (IMLS)rasmussen@ptd.net
Pages2884-2895
Number of pages12
ISBN (Print)9781510829008
StatePublished - Jan 1 2016
Externally publishedYes

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