An efficient sparse Bayesian learning algorithm based on Gaussian-scale mixtures
Sparse Bayesian learning (SBL) is a popular machine learning approach with a superior
generalization capability due to the sparsity of its adopted model. However, it entails a matrix …
generalization capability due to the sparsity of its adopted model. However, it entails a matrix …
Variational Bayesian inference of line spectra
MA Badiu, TL Hansen, BH Fleury - IEEE Transactions on Signal …, 2017 - ieeexplore.ieee.org
We address the fundamental problem of line spectral estimation in a Bayesian framework.
We target model order and parameter estimation via variational inference in a probabilistic …
We target model order and parameter estimation via variational inference in a probabilistic …
Spatiotemporal sparse Bayesian learning with applications to compressed sensing of multichannel physiological signals
Energy consumption is an important issue in continuous wireless telemonitoring of
physiological signals. Compressed sensing (CS) is a promising framework to address it, due …
physiological signals. Compressed sensing (CS) is a promising framework to address it, due …
Superfast line spectral estimation
TL Hansen, BH Fleury, BD Rao - IEEE Transactions on Signal …, 2018 - ieeexplore.ieee.org
A number of recent works have proposed to solve the line spectral estimation problem by
applying off-the-grid extensions of sparse estimation techniques. These methods are …
applying off-the-grid extensions of sparse estimation techniques. These methods are …
Fast inverse-free sparse Bayesian learning via relaxed evidence lower bound maximization
Sparse Beyesian learning is a popular approach for sparse signal recovery, and has
demonstrated superior performance in a series of experiments. Nevertheless, the sparse …
demonstrated superior performance in a series of experiments. Nevertheless, the sparse …
A clutter suppression algorithm via enhanced sparse Bayesian learning for airborne radar
D Wang, T Wang, W Cui, X Zhang - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
The traditional space–time adaptive processing (STAP) method based on sparse Bayesian
learning (SBL) has the problems of low computational efficiency and slow convergence …
learning (SBL) has the problems of low computational efficiency and slow convergence …
Model-based gas source localization strategy for a cooperative multi-robot system—A probabilistic approach and experimental validation incorporating physical …
Sampling gas distributions by robotic platforms in order to find gas sources is an appealing
approach to alleviate threats for a human operator. Different sampling strategies for robotic …
approach to alleviate threats for a human operator. Different sampling strategies for robotic …
Sparse estimation using Bayesian hierarchical prior modeling for real and complex linear models
In sparse Bayesian learning (SBL), Gaussian scale mixtures (GSMs) have been used to
model sparsity-inducing priors that realize a class of concave penalty functions for the …
model sparsity-inducing priors that realize a class of concave penalty functions for the …
Covariance-free sparse Bayesian learning
Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding
problem while also providing uncertainty quantification. The most popular inference …
problem while also providing uncertainty quantification. The most popular inference …
Gohberg-semencul factorization-based fast implementation of sparse Bayesian learning with a Fourier dictionary
F Dai, Y Wang, L Hong - IEEE Transactions on Geoscience and …, 2022 - ieeexplore.ieee.org
Sparse Bayesian learning (SBL) is a popular and robust algorithm for sparse signal
reconstruction (SSR). Unfortunately, the SBL algorithm suffers from heavy computational …
reconstruction (SSR). Unfortunately, the SBL algorithm suffers from heavy computational …