Consensus Based Distributed Sparse Bayesian Learning by Fast Marginal Likelihood Maximization
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2020.3039481
Abstract: For swarm systems, distributed processing is of paramount importance and Bayesian methods are preferred for their robustness. Existing distributed sparse Bayesian learning (SBL) methods rely on the automatic relevance determination (ARD), which involves a computationally… read more here.
Keywords: fast marginal; distributed sparse; likelihood maximization; marginal likelihood ... See more keywords
Distributed Sparse Undetectable Attacks Against State Estimation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Control of Network Systems"
DOI: 10.1109/tcns.2021.3112774
Abstract: This article studies a class of distributed attack strategies against state estimation of wireless sensor networks. The attack objective is to corrupt the least numbers of sensors so that the state estimate error approaches a… read more here.
Keywords: undetectable attacks; state; sparse undetectable; state estimation ... See more keywords
Distributed Sparse Optimization With Weakly Convex Regularizer: Consensus Promoting and Approximate Moreau Enhanced Penalties Towards Global Optimality
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Signal and Information Processing over Networks"
DOI: 10.1109/tsipn.2022.3181729
Abstract: We propose a promising framework for distributed sparse optimization based on weakly convex regularizers. More specifically, we pose two distributed optimization problems to recover sparse signals in networks. The first problem formulation relies on statistical… read more here.
Keywords: moreau enhanced; sparse optimization; weakly convex; approximate moreau ... See more keywords