Results 11 to 20 of about 114,074 (272)
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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Covariance-Free Sparse Bayesian Learning
IEEE Transactions on Signal Processing, 2022 Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem while also providing uncertainty quantification. The most popular inference algorithms for SBL exhibit prohibitively large computational costs for high-dimensional problems due to the need to maintain a large covariance matrix.
Alexander Lin +3 more
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On the sparse Bayesian learning of linear models [PDF]
Communications in Statistics - Theory and Methods, 2017 This work is a re-examination of the sparse Bayesian learning (SBL) of linear regression models of Tipping (2001) in a high-dimensional setting. We propose a hard-thresholded version of the SBL estimator that achieves, for orthogonal design matrices, the non-asymptotic estimation error rate of $σ\sqrt{s\log p}/\sqrt{n}$, where $n$ is the sample size ...
Atchade, Yves, Yee, Chia Chye
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Clustered sparse Bayesian learning [PDF]
, 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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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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Entropy, 2023 Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying ...
Mohammad Shekaramiz, Todd K. Moon
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An Improved Iterative Reweighted STAP Algorithm for Airborne Radar
Remote Sensing, 2022 In recent years, sparse recovery-based space-time adaptive processing (SR-STAP) technique has exhibited excellent performance with insufficient samples.
Weichen Cui +3 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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ISAR Imaging Algorithm for Parameter Iterative Minimization Sparse Signal Recovery [PDF]
Jisuanji gongcheng, 2018 In order to obtain the robustness Inverse Synthetic Aperture Radar(ISAR) image,an iterative minimization Bayesian learning sparse signal recovery algorithm is proposed.Firstly,ISAR imaging is established,and the imaging problem is converted to sparse ...
FENG Junjie,ZHANG Gong
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IEEE Access, 2021 This work discusses a variational Bayesian learning approach towards decentralized blind deconvolution of seismic signals within a sensor network. Blind seismic deconvolution is cast into a probabilistic framework based on Sparse Bayesian learning ...
Dmitriy Shutin, Ban-Sok Shin
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