Results 41 to 50 of about 154,546 (314)
A novel sparse representation algorithm for AIS real-time signals
EURASIP Journal on Wireless Communications and Networking, 2018 Sparse representation of signals based on a redundant dictionary is a new signal representation theory. Recent research activities in this field have concentrated mainly on the study of dictionary design and sparse decomposition algorithms.
Shuaiheng Huai, Shufang Zhang
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Adaptive Sparse Gaussian Process
IEEE Transactions on Neural Networks and Learning SystemsAdaptive learning is necessary for non-stationary environments where the learning machine needs to forget past data distribution. Efficient algorithms require a compact model update to not grow in computational burden with the incoming data and with the lowest possible computational cost for online parameter updating.
Vanessa Gómez-Verdejo +2 more
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Sparse Additive Gaussian Process Regression
J. Mach. Learn. Res., 2019 In this paper we introduce a novel model for Gaussian process (GP) regression in the fully Bayesian setting. Motivated by the ideas of sparsification, localization and Bayesian additive modeling, our model is built around a recursive partitioning (RP) scheme. Within each RP partition, a sparse GP (SGP) regression model is fitted.
Hengrui Luo +2 more
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Sparse within Sparse Gaussian Processes using Neighbor Information
CoRR, 2020 10 ...
Gia-Lac Tran +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 undercomplete dictionary learning task can be formulated as a blind separation problem of sparse sources linearly mixed with ...
Nicolas Dobigeon +3 more
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Sparse Gaussian Neural Processes
CoRRProceedings of the 7th Symposium on Advances in Approximate Bayesian Inference, PMLR, 2025.
Tommy Rochussen, Vincent Fortuin
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Sparse Algorithms for Markovian Gaussian Processes
CoRR, 2021 Approximate Bayesian inference methods that scale to very large datasets are crucial in leveraging probabilistic models for real-world time series. Sparse Markovian Gaussian processes combine the use of inducing variables with efficient Kalman filter-like recursions, resulting in algorithms whose computational and memory requirements scale linearly in ...
Wilkinson, William J. +2 more
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Leida xuebao, 2018 This paper examines the processing of millimeter-wave imaging data based on sparse sampling and sparse array design for the rapid imaging of human security data.
Tian He, Li Daojing, Qi Chunchao
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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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A Review of Radar Signal Processing Based on Sparse Recovery
Leida xuebaoWith the growing demand for radar target detection, Sparse Recovery (SR) technology based on the Compressive Sensing (CS) model has been widely used in radar signal processing.
Yinghui QUAN +6 more
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