Results 1 to 10 of about 17,359 (256)
Sparse bayesian learning for genomic selection in yeast [PDF]
Frontiers in Bioinformatics, 2022 Genomic selection, which predicts phenotypes such as yield and drought resistance in crops from high-density markers positioned throughout the genome of the varieties, is moving towards machine learning techniques to make predictions on complex traits ...
Maryam Ayat, Mike Domaratzki
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Compression Reconstruction and Fault Diagnosis of Diesel Engine Vibration Signal Based on Optimizing Block Sparse Bayesian Learning [PDF]
Sensors, 2022 It is critical to deploy wireless data transmission technologies remotely, in real-time, to monitor the health state of diesel engines dynamically. The usual approach to data compression is to collect data first, then compress it; however, we cannot ...
Huajun Bai +3 more
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A Fast Space-Time Adaptive Processing Algorithm Based on Sparse Bayesian Learning for Airborne Radar [PDF]
Sensors, 2022 Space-time adaptive processing (STAP) plays an essential role in clutter suppression and moving target detection in airborne radar systems. The main difficulty is that independent and identically distributed (i.i.d) training samples may not be sufficient
Cheng Liu +3 more
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Sparse Aperture InISAR Imaging via Sequential Multiple Sparse Bayesian Learning [PDF]
Sensors, 2017 Interferometric inverse synthetic aperture radar (InISAR) imaging for sparse-aperture (SA) data is still a challenge, because the similarity and matched degree between ISAR images from different channels are destroyed by the SA data.
Shuanghui Zhang, Yongxiang Liu, Xiang Li
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Sparse Bayesian learning with multiple dictionaries
Signal Processing, 2019 Abstract Sparse Bayesian learning (SBL) has emerged as a fast and competitive method to perform sparse processing. The SBL algorithm, which is developed using a Bayesian framework, iteratively solves a non-convex optimization problem using fixed point updates.
Kay L Gemba +2 more
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Alternative to Extended Block Sparse Bayesian Learning and Its Relation to Pattern-Coupled Sparse Bayesian Learning [PDF]
IEEE Transactions on Signal Processing, 2018 We consider the problem of recovering block sparse signals with unknown block partition and propose a better alternative to the extended block sparse Bayesian learning (EBSBL). The underlying relationship between the proposed method EBSBL and pattern-coupled sparse Bayesian learning (PC-SBL) is explicitly revealed.
Lu Wang, Lifan Zhao, Guoan Bi
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Sparse Bayesian Learning for DOA Estimation with Mutual Coupling [PDF]
Sensors, 2015 Sparse Bayesian learning (SBL) has given renewed interest to the problem of direction-of-arrival (DOA) estimation. It is generally assumed that the measurement matrix in SBL is precisely known.
Jisheng Dai +3 more
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Underwater Acoustic Channel Estimation Based on Sparse Bayesian Learning Algorithm
IEEE Access, 2023 The channel estimation algorithm based on sparse Bayesian learning proposed in recent years shows better performance than the traditional channel estimation algorithm by effectively reducing the convergence error in the channel estimation process ...
Shuyang Jia +3 more
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Bayesian learning of sparse classifiers [PDF]
Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2005 Bayesian approaches to supervised learning use priors on the classifier parameters. However, few priors aim at achieving "sparse" classifiers, where irrelevant/redundant parameters are automatically set to zero. Two well-known ways of obtaining sparse classifiers are: use a zero-mean Laplacian prior on the parameters, and the "support vector machine ...
Mário A. T. Figueiredo +1 more
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Multisnapshot Sparse Bayesian Learning for DOA [PDF]
IEEE Signal Processing Letters, 2016 The directions of arrival (DOA) of plane waves are estimated from multisnapshot sensor array data using sparse Bayesian learning (SBL). The prior for the source amplitudes is assumed independent zero-mean complex Gaussian distributed with hyperparameters, the unknown variances (i.e., the source powers).
Peter Gerstoft +3 more
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