Recently, tensor sparsity modeling has achieved great success in the tensor completion
(TC) problem. In real applications, the sparsity of a tensor can be rationally measured by low …

In this letter, we propose a novel sparse Bayesian learning (SBL) algorithm using
Generalized Double Pareto (GDP) prior to enhance the performance of direction of arrival …

As a powerful tool in analyzing multi-dimensional data, tensor train (TT) decomposition
shows superior performance compared to other tensor decomposition formats. Existing TT …

Adapting the space-time adaptive processing (STAP) filter with finite number of secondary
data is of particular interest for airborne phased-array radar clutter suppression. Sparse …

In this letter, we propose a multi-task compressive sensing algorithm for the reconstruction of
clustered sparse entries based on hierarchical Bayesian framework. By extending a paired …

Tensor rank learning for canonical polyadic decomposition (CPD) has long been deemed as
an essential yet challenging problem. In particular, since thetensor rank controls the …

Regression in a sparse Bayesian learning (SBL) framework is usually formulated as a global
optimization problem with a nonconvex objective function and solved in a majorization …

In this paper, we develop a novel structured Bayesian compressive sensing algorithm with
location dependence for high-resolution imaging in ultra-narrowband passive synthetic …

Tucker decomposition is the cornerstone of modern machine learning on tensorial data
analysis, which have attracted considerable attention for multiway feature extraction …

In this paper we present a novel wavelet-based Bayesian nonparametric regression model
for the analysis of functional magnetic resonance imaging (fMRI) data. Our goal is to provide …