Relevant bibliographies by topics / Sparse Bayesian learning (SBL)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Sparse Bayesian learning (SBL)'

Author: Grafiati
Published: 24 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Sparse Bayesian learning (SBL)"

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Yuan, Cheng, and Mingjun Su. "Seismic spectral sparse reflectivity inversion based on SBL-EM: experimental analysis and application." Journal of Geophysics and Engineering 16, no. 6 (2019): 1124–38. http://dx.doi.org/10.1093/jge/gxz082.

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Abstract In this paper, we propose a new method of seismic spectral sparse reflectivity inversion that, for the first time, introduces Expectation-Maximization-based sparse Bayesian learning (SBL-EM) to enhance the accuracy of stratal reflectivity estimation based on the frequency spectrum of seismic reflection data. Compared with the widely applied sequential algorithm-based sparse Bayesian learning (SBL-SA), SBL-EM is more robust to data noise and, generally, can not only find a sparse solution with higher precision, but also yield a better lateral continuity along the final profile. To inve
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Liu, Qinghua, Yuanxin He, Kai Ding, and Quanmin Xie. "Complex Multisnapshot Sparse Bayesian Learning for Offgrid DOA Estimation." International Journal of Antennas and Propagation 2022 (February 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/4500243.

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Direction of arrival (DOA) estimation has recently been developed based on sparse signal reconstruction (SSR). Sparse Bayesian learning (SBL) is a typical method of SSR. In SBL, the two-layer hierarchical model in Gaussian scale mixtures (GSMs) has been used to model sparsity-inducing priors. However, this model is mainly applied to real-valued signal models. In order to apply SBL to complex-valued signal models, a general class of sparsity-inducing priors is proposed for complex-valued signal models by complex Gaussian scale mixtures (CGSMs), and the special cases correspond to complex versio
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Shin, Myoungin, Wooyoung Hong, Keunhwa Lee, and Youngmin Choo. "Frequency Analysis of Acoustic Data Using Multiple-Measurement Sparse Bayesian Learning." Sensors 21, no. 17 (2021): 5827. http://dx.doi.org/10.3390/s21175827.

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Passive sonar systems are used to detect the acoustic signals that are radiated from marine objects (e.g., surface ships, submarines, etc.), and an accurate estimation of the frequency components is crucial to the target detection. In this paper, we introduce sparse Bayesian learning (SBL) for the frequency analysis after the corresponding linear system is established. Many algorithms, such as fast Fourier transform (FFT), estimate signal parameters via rotational invariance techniques (ESPRIT), and multiple signal classification (RMUSIC) has been proposed for frequency detection. However, the
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Liu, Kun, Tong Wang, Jianxin Wu, Cheng Liu, and Weichen Cui. "On the Efficient Implementation of Sparse Bayesian Learning-Based STAP Algorithms." Remote Sensing 14, no. 16 (2022): 3931. http://dx.doi.org/10.3390/rs14163931.

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Sparse Bayesian learning-based space–time adaptive processing (SBL-STAP) algorithms can achieve superior clutter suppression performance with limited training sample support in practical heterogeneous and non-stationary clutter environments. However, when the system has high degrees of freedom (DOFs), SBL-STAP algorithms suffer from high computational complexity, since the large-scale matrix calculations and the inversion operations of large-scale covariance matrices are involved in the iterative process. In this article, we consider a computationally efficient implementation for SBL-STAP algo
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Shin, Myoungin, Wooyoung Hong, Keunhwa Lee, and Youngmin Choo. "Passive Sonar Target Identification Using Multiple-Measurement Sparse Bayesian Learning." Sensors 22, no. 21 (2022): 8511. http://dx.doi.org/10.3390/s22218511.

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Accurate estimation of the frequency component is an important issue to identify and track marine objects (e.g., surface ship, submarine, etc.). In general, a passive sonar system consists of a sensor array, and each sensor receives data that have common information of the target signal. In this paper, we consider multiple-measurement sparse Bayesian learning (MM-SBL), which reconstructs sparse solutions in a linear system using Bayesian frameworks, to detect the common frequency components received by each sensor. In addition, the direction of arrival estimation was performed on each detected
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Wang, Hongyan, Yanping Bai, Jing Ren, et al. "DOA Estimation Method for Vector Hydrophones Based on Sparse Bayesian Learning." Sensors 24, no. 19 (2024): 6439. http://dx.doi.org/10.3390/s24196439.

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Through extensive literature review, it has been found that sparse Bayesian learning (SBL) is mainly applied to traditional scalar hydrophones and is rarely applied to vector hydrophones. This article proposes a direction of arrival (DOA) estimation method for vector hydrophones based on SBL (Vector-SBL). Firstly, vector hydrophones capture both sound pressure and particle velocity, enabling the acquisition of multidimensional sound field information. Secondly, SBL accurately reconstructs the received vector signal, addressing challenges like low signal-to-noise ratio (SNR), limited snapshots,
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Ali K., Shoukath, Arfat Ahmad Khan, Perarasi T, Ateeq Ur Rehman, and Khmaies Ouahada. "Learned-SBL-GAMP based hybrid precoders/combiners in millimeter wave massive MIMO systems." PLOS ONE 18, no. 9 (2023): e0289868. http://dx.doi.org/10.1371/journal.pone.0289868.

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In Millimeter-Wave (mm-Wave) massive Multiple-Input Multiple-Output (MIMO) systems, hybrid precoders/combiners must be designed to improve antenna gain and reduce hardware complexity. Sparse Bayesian learning via Expectation Maximization (SBL-EM) algorithm is not practically feasible for high signal dimensions because estimating sparse signals and designing optimal hybrid precoders/combiners using SBL-EM still provide high computational complexity for higher signal dimensions. To overcome the issues of high computational complexity along with making it suitable for larger data sets, in this pa
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Wang, Jinyang, El-Bay Bourennane, Mahdi Madani, et al. "High-Throughput MPSoC Implementation of Sparse Bayesian Learning Algorithm." Electronics 13, no. 1 (2024): 234. http://dx.doi.org/10.3390/electronics13010234.

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In the field of sparse signal reconstruction, sparse Bayesian learning (SBL) has excellent performance, which is accompanied by extremely high computational complexity. This paper presents an efficient SBL hardware and software (HW&SW) co-implementation method using the ZYNQ series MPSoC (multiprocessor system-on-chip). Firstly, considering the inherent challenges in parallelizing iterative algorithms like SBL, we propose an architecture based on the iterative calculations implemented on the PL side (FPGA) and the iteration control and input management handled by the PS side (ARM). By adop
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Cui, Weichen, Tong Wang, Degen Wang, and Kun Liu. "An Efficient Sparse Bayesian Learning STAP Algorithm with Adaptive Laplace Prior." Remote Sensing 14, no. 15 (2022): 3520. http://dx.doi.org/10.3390/rs14153520.

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Space-time adaptive processing (STAP) encounters severe performance degradation with insufficient training samples in inhomogeneous environments. Sparse Bayesian learning (SBL) algorithms have attracted extensive attention because of their robust and self-regularizing nature. In this study, a computationally efficient SBL STAP algorithm with adaptive Laplace prior is developed. Firstly, a hierarchical Bayesian model with adaptive Laplace prior for complex-value space-time snapshots (CALM-SBL) is formulated. Laplace prior enforces the sparsity more heavily than Gaussian, which achieves a better
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Cho, Yong-Ho. "Fast Sparse Bayesian Learning-Based Channel Estimation for Underwater Acoustic OFDM Systems." Applied Sciences 12, no. 19 (2022): 10175. http://dx.doi.org/10.3390/app121910175.

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Harsh underwater channels and energy constraints are the two critical issues of underwater acoustic (UWA) communications. To achieve a high channel estimation performance under a severe underwater channel, sparse Bayesian learning (SBL)-based channel estimation was adopted for UWA orthogonal frequency division multiplexing (OFDM) systems. Accurate channel estimation can guarantee the successful reception of transmitted data and reduce retransmission occurrences, thereby, leading to energy-efficient communications. However, SBL-based algorithms have improved performances in iterative ways, whic
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Dissertations / Theses on the topic "Sparse Bayesian learning (SBL)"

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Chen, Cong. "High-Dimensional Generative Models for 3D Perception." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103948.

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Modern robotics and automation systems require high-level reasoning capability in representing, identifying, and interpreting the three-dimensional data of the real world. Understanding the world's geometric structure by visual data is known as 3D perception. The necessity of analyzing irregular and complex 3D data has led to the development of high-dimensional frameworks for data learning. Here, we design several sparse learning-based approaches for high-dimensional data that effectively tackle multiple perception problems, including data filtering, data recovery, and data retrieval. The fram
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Kurisummoottil, Thomas Christo. "Sparse Bayesian learning, beamforming techniques and asymptotic analysis for massive MIMO." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS231.

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Des antennes multiples du côté de la station de base peuvent être utilisées pour améliorer l'efficacité spectrale et l'efficacité énergétique des technologies sans fil de nouvelle génération. En effet, le multi-entrées et sorties multiples massives (MIMO) est considéré comme une technologie prometteuse pour apporter les avantages susmentionnés pour la norme sans fil de cinquième génération, communément appelée 5G New Radio (5G NR). Dans cette monographie, nous explorerons un large éventail de sujets potentiels dans Multi-userMIMO (MU-MIMO) pertinents pour la 5G NR,• Conception de la formation
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Higson, Edward John. "Bayesian methods and machine learning in astrophysics." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/289728.

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This thesis is concerned with methods for Bayesian inference and their applications in astrophysics. We principally discuss two related themes: advances in nested sampling (Chapters 3 to 5), and Bayesian sparse reconstruction of signals from noisy data (Chapters 6 and 7). Nested sampling is a popular method for Bayesian computation which is widely used in astrophysics. Following the introduction and background material in Chapters 1 and 2, Chapter 3 analyses the sampling errors in nested sampling parameter estimation and presents a method for estimating them numerically for a single nested sam
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Jin, Junyang. "Novel methods for biological network inference : an application to circadian Ca2+ signaling network." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/285323.

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Biological processes involve complex biochemical interactions among a large number of species like cells, RNA, proteins and metabolites. Learning these interactions is essential to interfering artificially with biological processes in order to, for example, improve crop yield, develop new therapies, and predict new cell or organism behaviors to genetic or environmental perturbations. For a biological process, two pieces of information are of most interest. For a particular species, the first step is to learn which other species are regulating it. This reveals topology and causality. The second
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Subramanian, Harshavardhan. "Combining scientific computing and machine learning techniques to model longitudinal outcomes in clinical trials." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176427.

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Scientific machine learning (SciML) is a new branch of AI research at the edge of scientific computing (Sci) and machine learning (ML). It deals with efficient amalgamation of data-driven algorithms along with scientific computing to discover the dynamics of the time-evolving process. The output of such algorithms is represented in the form of a governing equation(s) (e.g., ordinary differential equation(s), ODE(s)), which one can solve then for any time point and, thus, obtain a rigorous prediction.  In this thesis, we present a methodology on how to incorporate the SciML approach in the cont
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Francisco, André Biasin Segalla. "Esparsidade estruturada em reconstrução de fontes de EEG." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/43/43134/tde-13052018-112615/.

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Neuroimagiologia funcional é uma área da neurociência que visa o desenvolvimento de diversas técnicas para mapear a atividade do sistema nervoso e esteve sob constante desenvolvimento durante as últimas décadas devido à sua grande importância para aplicações clínicas e pesquisa. Técnicas usualmente utilizadas, como imagem por ressonância magnética functional (fMRI) e tomografia por emissão de pósitrons (PET) têm ótima resolução espacial (~ mm), mas uma resolução temporal limitada (~ s), impondo um grande desafio para nossa compreensão a respeito da dinâmica de funções cognitivas mais elevadas,
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Le, Folgoc Loïc. "Apprentissage statistique pour la personnalisation de modèles cardiaques à partir de données d’imagerie." Thesis, Nice, 2015. http://www.theses.fr/2015NICE4098/document.

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Cette thèse porte sur un problème de calibration d'un modèle électromécanique de cœur, personnalisé à partir de données d'imagerie médicale 3D+t ; et sur celui - en amont - de suivi du mouvement cardiaque. A cette fin, nous adoptons une méthodologie fondée sur l'apprentissage statistique. Pour la calibration du modèle mécanique, nous introduisons une méthode efficace mêlant apprentissage automatique et une description statistique originale du mouvement cardiaque utilisant la représentation des courants 3D+t. Notre approche repose sur la construction d'un modèle statistique réduit reliant l'esp
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Dang, Hong-Phuong. "Approches bayésiennes non paramétriques et apprentissage de dictionnaire pour les problèmes inverses en traitement d'image." Thesis, Ecole centrale de Lille, 2016. http://www.theses.fr/2016ECLI0019/document.

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L'apprentissage de dictionnaire pour la représentation parcimonieuse est bien connu dans le cadre de la résolution de problèmes inverses. Les méthodes d'optimisation et les approches paramétriques ont été particulièrement explorées. Ces méthodes rencontrent certaines limitations, notamment liées au choix de paramètres. En général, la taille de dictionnaire doit être fixée à l'avance et une connaissance des niveaux de bruit et éventuellement de parcimonie sont aussi nécessaires. Les contributions méthodologies de cette thèse concernent l'apprentissage conjoint du dictionnaire et de ces paramètr
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Gerchinovitz, Sébastien. "Prédiction de suites individuelles et cadre statistique classique : étude de quelques liens autour de la régression parcimonieuse et des techniques d'agrégation." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00653550.

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Cette thèse s'inscrit dans le domaine de l'apprentissage statistique. Le cadre principal est celui de la prévision de suites déterministes arbitraires (ou suites individuelles), qui recouvre des problèmes d'apprentissage séquentiel où l'on ne peut ou ne veut pas faire d'hypothèses de stochasticité sur la suite des données à prévoir. Cela conduit à des méthodes très robustes. Dans ces travaux, on étudie quelques liens étroits entre la théorie de la prévision de suites individuelles et le cadre statistique classique, notamment le modèle de régression avec design aléatoire ou fixe, où les données
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Shi, Minghui. "Bayesian Sparse Learning for High Dimensional Data." Diss., 2011. http://hdl.handle.net/10161/3869.

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<p>In this thesis, we develop some Bayesian sparse learning methods for high dimensional data analysis. There are two important topics that are related to the idea of sparse learning -- variable selection and factor analysis. We start with Bayesian variable selection problem in regression models. One challenge in Bayesian variable selection is to search the huge model space adequately, while identifying high posterior probability regions. In the past decades, the main focus has been on the use of Markov chain Monte Carlo (MCMC) algorithms for these purposes. In the first part of this thesis, i
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Book chapters on the topic "Sparse Bayesian learning (SBL)"

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Chatzis, Sotirios P. "Sparse Bayesian Recurrent Neural Networks." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23525-7_22.

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Naik, Cian, François Caron, Judith Rousseau, Yee Whye Teh, and Konstantina Palla. "Bayesian Nonparametrics for Sparse Dynamic Networks." In Machine Learning and Knowledge Discovery in Databases. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26419-1_12.

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Huang, Yong, and James L. Beck. "Sparse Bayesian Learning and its Application in Bayesian System Identification." In Bayesian Inverse Problems. CRC Press, 2021. http://dx.doi.org/10.1201/b22018-7.

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Zhang, Guanghao, Dongshun Cui, Shangbo Mao, and Guang-Bin Huang. "Sparse Bayesian Learning for Extreme Learning Machine Auto-encoder." In Proceedings in Adaptation, Learning and Optimization. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23307-5_34.

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Mongwe, Wilson Tsakane, Rendani Mbuvha, and Tshilidzi Marwala. "Learning Equity Volatility Surfaces Using Sparse Gaussian Processes." In Bayesian Machine Learning in Quantitative Finance. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88431-3_4.

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Lei, Yun, Xiaoqing Ding, and Shengjin Wang. "Adaptive Sparse Vector Tracking Via Online Bayesian Learning." In Advances in Machine Vision, Image Processing, and Pattern Analysis. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11821045_4.

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Mongwe, Wilson Tsakane, Rendani Mbuvha, and Tshilidzi Marwala. "Sparse and Distributed Gaussian Processes for Modeling Corporate Credit Ratings." In Bayesian Machine Learning in Quantitative Finance. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88431-3_6.

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Michel, Vincent, Evelyn Eger, Christine Keribin, and Bertrand Thirion. "Multi-Class Sparse Bayesian Regression for Neuroimaging Data Analysis." In Machine Learning in Medical Imaging. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15948-0_7.

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Wang, Lu, Lifan Zhao, Guoan Bi, and Xin Liu. "Alternative Extended Block Sparse Bayesian Learning for Cluster Structured Sparse Signal Recovery." In Wireless and Satellite Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19153-5_1.

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Sabuncu, Mert R. "A Sparse Bayesian Learning Algorithm for Longitudinal Image Data." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24574-4_49.

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Conference papers on the topic "Sparse Bayesian learning (SBL)"

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Zhao, Yonghong, Jisong Liu, Junlong wang, and Shuxin Dong. "A joint sparse Bayesian learning for wideband DOA estimation." In Conference on Spectral Technology and Applications (CSTA 2024), edited by Zhe Wang and Hongbin Ding. SPIE, 2024. https://doi.org/10.1117/12.3037518.

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Tung, Alex, and Peter Gerstoft. "Multipole Source Capture Using Multiple Dictionary Sparse Bayesian Learning." In 2024 58th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2024. https://doi.org/10.1109/ieeeconf60004.2024.10942864.

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Zhang, Junyan, Zonglong Bai, Zhaoyi Liao, and Zhiyuan Xie. "Sparse Bayesian Learning Approach for Wide-band Acoustic Imaging." In 2024 7th International Conference on Information Communication and Signal Processing (ICICSP). IEEE, 2024. https://doi.org/10.1109/icicsp62589.2024.10809118.

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Srivastava, Suraj, Ch Suraj Kumar Patro, Aditya K. Jagannatham, and Govind Sharma. "Sparse Bayesian Learning (SBL)-Based Frequency-Selective Channel Estimation for Millimeter Wave Hybrid MIMO Systems." In 2019 National Conference on Communications (NCC). IEEE, 2019. http://dx.doi.org/10.1109/ncc.2019.8732197.

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Srivastava, Suraj, and Aditya K. Jagannatham. "Sparse Bayesian Learning-Based Kalman Filtering (SBL-KF) for Group-Sparse Channel Estimation in Doubly Selective mmWave Hybrid MIMO Systems." In 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2019. http://dx.doi.org/10.1109/spawc.2019.8815509.

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Várady Filho, C. A. F., J. K. F. Tenório, E. T. Lima, J. P. L. Santos, R. Dias, and F. S. Cutrim. "Application of Data-Driven Techniques in 3D Soil Characterization for Top-Hole Design." In Offshore Technology Conference. OTC, 2025. https://doi.org/10.4043/35896-ms.

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Abstract Present work investigated the application of 3D Sparse Bayesian Learning (SBL) and Gaussian Process Regression (GPR) for data-driven offshore site characterization focusing specifically on estimating critical soil parameters for top-hole design in oil and gas wells, such as undrained shear resistance and specific unit weight. These parameters are essential for designing conductor and surface casing sections in drilling operations and ensuring the structural integrity of wellhead systems and casing strings. This study used datasets of piezocone penetration tests (CPTu) conducted in the
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Qiao, Xuechun, and Yasen Wang. "Recursive Sparse Bayesian Learning." In 2022 China Automation Congress (CAC). IEEE, 2022. http://dx.doi.org/10.1109/cac57257.2022.10055431.

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Chang, Xiao, and Qinghua Zheng. "Sparse Bayesian learning for ranking." In 2009 IEEE International Conference on Granular Computing (GRC). IEEE, 2009. http://dx.doi.org/10.1109/grc.2009.5255164.

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Giri, Ritwik, and Bhaskar D. Rao. "Bootstrapped sparse Bayesian learning for sparse signal recovery." In 2014 48th Asilomar Conference on Signals, Systems and Computers. IEEE, 2014. http://dx.doi.org/10.1109/acssc.2014.7094748.

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Park, Yongsung, Florian Meyer, and Peter Gerstoft. "Sequential Sparse Bayesian Learning For Doa." In 2020 54th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2020. http://dx.doi.org/10.1109/ieeeconf51394.2020.9443444.

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