When Laplacian scale mixture meets three-layer transform: A parametric tensor sparsity for tensor completion
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 …
(TC) problem. In real applications, the sparsity of a tensor can be rationally measured by low …
Sparse Bayesian learning using generalized double Pareto prior for DOA estimation
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 …
Generalized Double Pareto (GDP) prior to enhance the performance of direction of arrival …
Tensor train factorization under noisy and incomplete data with automatic rank estimation
As a powerful tool in analyzing multi-dimensional data, tensor train (TT) decomposition
shows superior performance compared to other tensor decomposition formats. Existing TT …
shows superior performance compared to other tensor decomposition formats. Existing TT …
Clutter suppression algorithm based on fast converging sparse Bayesian learning for airborne radar
Z Wang, W Xie, K Duan, Y Wang - Signal Processing, 2017 - Elsevier
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 …
data is of particular interest for airborne phased-array radar clutter suppression. Sparse …
Multi-task Bayesian compressive sensing exploiting intra-task dependency
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 …
clustered sparse entries based on hierarchical Bayesian framework. By extending a paired …
Towards flexible sparsity-aware modeling: Automatic tensor rank learning using the generalized hyperbolic prior
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 …
an essential yet challenging problem. In particular, since thetensor rank controls the …
Sparse Bayesian learning based on collaborative neurodynamic optimization
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 …
optimization problem with a nonconvex objective function and solved in a majorization …
High-resolution passive SAR imaging exploiting structured Bayesian compressive sensing
In this paper, we develop a novel structured Bayesian compressive sensing algorithm with
location dependence for high-resolution imaging in ultra-narrowband passive synthetic …
location dependence for high-resolution imaging in ultra-narrowband passive synthetic …
Bayesian sparse tucker models for dimension reduction and tensor completion
Tucker decomposition is the cornerstone of modern machine learning on tensorial data
analysis, which have attracted considerable attention for multiway feature extraction …
analysis, which have attracted considerable attention for multiway feature extraction …
A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses
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 …
for the analysis of functional magnetic resonance imaging (fMRI) data. Our goal is to provide …