Results 21 to 30 of about 114,074 (272)
A Robust Sparse Bayesian Learning-Based DOA Estimation Method With Phase Calibration
IEEE Access, 2020 Usually, the array manifolds are assumed to be known perfectly in the radar systems, but the imprecise knowledge substantially degrades the performance of estimating the direction of arrival (DOA).
Zhimin Chen +3 more
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Low-Rank and Sparse Matrix Recovery for Hyperspectral Image Reconstruction Using Bayesian Learning
Sensors, 2022 In order to reduce the amount of hyperspectral imaging (HSI) data transmission required through hyperspectral remote sensing (HRS), we propose a structured low-rank and joint-sparse (L&S) data compression and reconstruction method.
Yanbin Zhang +4 more
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Sensors, 2020 Process monitoring plays an important role in ensuring the safety and stable operation of equipment in a large-scale process. This paper proposes a novel data-driven process monitoring framework, termed the ensemble adaptive sparse Bayesian transfer ...
Hongchao Cheng +4 more
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npj Computational Materials, 2023 Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics.
Yu Xie +5 more
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Multimodal Sparse Bayesian Dictionary Learning
CoRR, 2018 This paper addresses the problem of learning dictionaries for multimodal datasets, i.e. datasets collected from multiple data sources. We present an algorithm called multimodal sparse Bayesian dictionary learning (MSBDL). MSBDL leverages information from all available data modalities through a joint sparsity constraint.
Igor Fedorov, Bhaskar D. Rao
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Relevance Vector Machines for Enhanced BER Probability in DMT-Based Systems
Journal of Electrical and Computer Engineering, 2010 A new channel estimation method for discrete multitone (DMT) communication system based on sparse Bayesian learning relevance vector machine (RVM) method is presented.
Ashraf A. Tahat, Nikolaos P. Galatsanos
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Variational Bayesian Sparse Signal Recovery With LSM Prior
IEEE Access, 2017 This paper presents a new sparse signal recovery algorithm using variational Bayesian inference based on the Laplace approximation. The sparse signal is modeled as the Laplacian scale mixture (LSM) prior.
Shuanghui Zhang +3 more
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Bayesian orthogonal component analysis for sparse representation [PDF]
, 2010 This paper addresses the problem of identifying a lower dimensional space where observed data can be sparsely represented. This under-complete dictionary learning task can be formulated as a blind separation problem of sparse sources linearly mixed with ...
Dobigeon, Nicolas, Tourneret, Jean-Yves
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Identification of nonlinear sparse networks using sparse Bayesian learning [PDF]
2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017 This paper considers a parametric approach to infer sparse networks described by nonlinear ARX models, with linear ARX treated as a special case. The proposed method infers both the Boolean structure and the internal dynamics of the network. It considers classes of nonlinear systems that can be written as weighted (unknown) sums of nonlinear functions ...
Junyang Jin +5 more
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Sparse Bayesian Modeling With Adaptive Kernel Learning [PDF]
IEEE Transactions on Neural Networks, 2009 Sparse kernel methods are very efficient in solving regression and classification problems. The sparsity and performance of these methods depend on selecting an appropriate kernel function, which is typically achieved using a cross-validation procedure.
Tzikas, D. G. +2 more
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