Spatio-Temporal Structured Sparse Regression with Hierarchical Gaussian Process Priors
This work addresses source localization and object detection problems with sparse signals, representing an incremental advance with a novel method for a known bottleneck.
The paper tackles the problem of reconstructing spatio-temporal evolving patterns from sparse signals by introducing a new sparse spatio-temporal structured Gaussian process regression framework, achieving a 15% improvement in F-measure compared to existing methods and reducing memory requirements.
This paper introduces a new sparse spatio-temporal structured Gaussian process regression framework for online and offline Bayesian inference. This is the first framework that gives a time-evolving representation of the interdependencies between the components of the sparse signal of interest. A hierarchical Gaussian process describes such structure and the interdependencies are represented via the covariance matrices of the prior distributions. The inference is based on the expectation propagation method and the theoretical derivation of the posterior distribution is provided in the paper. The inference framework is thoroughly evaluated over synthetic, real video and electroencephalography (EEG) data where the spatio-temporal evolving patterns need to be reconstructed with high accuracy. It is shown that it achieves 15% improvement of the F-measure compared with the alternating direction method of multipliers, spatio-temporal sparse Bayesian learning method and one-level Gaussian process model. Additionally, the required memory for the proposed algorithm is less than in the one-level Gaussian process model. This structured sparse regression framework is of broad applicability to source localisation and object detection problems with sparse signals.