Structured Sparse Modelling with Hierarchical GP
This work addresses structured sparse modeling for applications requiring spatio-temporal data analysis, but it appears incremental as it builds on existing Bayesian and sparse regression techniques.
The authors tackled the problem of sparse linear regression with spatio-temporal structure by proposing a new Bayesian model using a hierarchical Gaussian process prior for spike and slab coefficients, and they evaluated it on real data.
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.