Spatial Shrinkage Via the Product Independent Gaussian Process Prior

We study the problem of sparse signal detection on a spatial domain. We propose a novel approach to model continuous signals that are sparse and piecewise-smooth as the product of independent Gaussian (PING) processes with a smooth covariance kernel. The smoothness of the PING process is ensured by the smoothness of the covariance kernels of the Gaussian components in the product, and sparsity is controlled by the number of components. The bivariate kurtosis of the PING process implies that more components in the product results in the thicker tail and sharper peak at zero. We develop an efficient computation algorithm based on spectral methods. The simulation results demonstrate superior estimation using the PING prior over Gaussian process prior for different image regressions. We apply our method to a longitudinal magnetic resonance imaging dataset to detect the regions that are affected by multiple sclerosis computation in this domain. for this article are available online.

Automated summary

This study introduces a novel approach for sparse signal detection on a spatial domain by modeling continuous signals as the product of independent Gaussian processes. The proposed method achieves better control over signal smoothness and sparsity using PING processes, resulting in improved estimation accuracy for image regressions.