Nonlinear Statistical Learning with Truncated Gaussian Graphical Models
By Qinliang Su, Xuejun Liao, Changyou Chen, Lawrence Carin. 2016
12 citations recorded by the source corpus.
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 nonnegative. The truncated variables are assumed latent and integrated out to induce a marginal model. We show that the variables in the marginal model are 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.
Subfields: Analytical Chemistry, Artificial Intelligence, Probabilistic & Bayesian Methods
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In Analytical Chemistry
In Artificial Intelligence
- Davide Scaramuzza
- Antonio Loquercio
- Mattia Segu
- Stefan Roth
- Jochen Gast
- Thomas Bo Schön
- Fredrik K. Gustafsson
- Martin Danelljan
In Probabilistic & Bayesian Methods
- Mattia Segu
- Antonio Loquercio
- Davide Scaramuzza
- Stefan Roth
- Jochen Gast
- Fredrik K. Gustafsson
- Thomas Bo Schön
- Martin Danelljan
Citations in the corpus
- A General Framework for Uncertainty Estimation in Deep Learning (2019, background): “Consequently, networks’ predictions can also be represented by the same distributions, and can be analytically computed using non-linear belief networks [5] or graphical models [10].”
- Lightweight Probabilistic Deep Networks (2018, methodology): “[48] use a truncated Gaussian graphical model in an expectation maximization framework.”