Structured Sparse Modelling with Hierarchical GP
Machine Learning
2017-05-01 v1
Abstract
In this paper a new Bayesian model for sparse linear regression with a spatio-temporal structure is proposed. It incorporates the structural assumptions based on a hierarchical Gaussian process prior for spike and slab coefficients. We design an inference algorithm based on Expectation Propagation and evaluate the model over the real data.
Cite
@article{arxiv.1704.08727,
title = {Structured Sparse Modelling with Hierarchical GP},
author = {Danil Kuzin and Olga Isupova and Lyudmila Mihaylova},
journal= {arXiv preprint arXiv:1704.08727},
year = {2017}
}
Comments
SPARS 2017