Results 31 to 40 of about 6,810,122 (281)
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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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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Joint Estimation for DOA and Polarization Parameters in Sparse Bayesian Framework [PDF]
Hangkong bingqi, 2021 Aiming at the problems of low precision and high computational complexity in estimating coherent signals by traditional polarization sensitive array, a joint parameter estimation algorithm based on sparse Bayesian learning framework for direction of ...
Xu Haifeng
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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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Hierarchical Bayesian sparse image reconstruction with application to MRFM [PDF]
, 2008 This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally
Hero, Alfred O. +2 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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Hierarchic Bayesian models for kernel learning [PDF]
, 2005 The integration of diverse forms of informative data by learning an optimal combination of base kernels in classification or regression problems can provide enhanced performance when compared to that obtained from any single data source.
Rogers, S. +3 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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Clustered sparse Bayesian learning
, 2015 Many machine learning and signal processing tasks involve computing sparse representations using an overcomplete set of features or basis vectors, with compressive sensing-based applications a notable example. While traditionally such problems have been solved individually for different tasks, this strategy ignores strong correlations that may be ...
Wang, Y +4 more
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IEEE Access, 2019 Micro Doppler analysis of spin stabilized objects is of a great significance for attitude estimation and recognition of space targets. In practice, the radar cannot dwell on one target in a long interval continuously.
Ling Hong, Fengzhou Dai, Xili Wang
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