Results 41 to 50 of about 6,810,122 (281)
Semi-blind sparse image reconstruction with application to MRFM [PDF]
, 2012 We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known.
Hero, Alfred O. +2 more
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Iterative Temporal Learning and Prediction with the Sparse Online Echo State Gaussian Process [PDF]
, 2012 16/01/14 meb. pre-print version OK to add. statement added.In this work, we contribute the online echo state gaussian process (OESGP), a novel Bayesian-based online method that is capable of iteratively learning complex temporal dynamics and producing ...
Soh, Harold +3 more
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IET Radar, Sonar & Navigation, 2022 The shallow water noise shows obvious impulsive property, which greatly degrades the direction of arrival (DOA) performance due to the conventional design concept based on the Gaussian assumption.
Xiao Feng +5 more
doaj +1 more source
Structural Learning of Activities from Sparse Datasets [PDF]
, 2007 A major challenge in pervasive computing is to learn activity patterns, such as bathing and cleaning from sensor data. Typical sensor deployments generate sparse datasets with thousands of sensor readings and a few instances of activities.
Friday, Adrian +2 more
core +5 more sources
EM-based parameter iterative approach for sparse Bayesian channel estimation of massive MIMO system
EURASIP Journal on Wireless Communications and Networking, 2017 One of the main challenges for a massive multi-input multi-output (MIMO) system is to obtain accurate channel state information despite the increasing number of antennas at the base station.
Sulin Mei, Yong Fang
doaj +1 more source
Multimodal Sparse Bayesian Dictionary Learning
CoRR, 2018 This paper addresses the problem of learning dictionaries for multimodal datasets, i.e. datasets collected from multiple data sources. We present an algorithm called multimodal sparse Bayesian dictionary learning (MSBDL). MSBDL leverages information from all available data modalities through a joint sparsity constraint.
Igor Fedorov, Bhaskar D. Rao
openaire +2 more sources
Sparse Bayesian Learning via Stepwise Regression
CoRR, 2021 Sparse Bayesian Learning (SBL) is a powerful framework for attaining sparsity in probabilistic models. Herein, we propose a coordinate ascent algorithm for SBL termed Relevance Matching Pursuit (RMP) and show that, as its noise variance parameter goes to zero, RMP exhibits a surprising connection to Stepwise Regression.
Sebastian E. Ament, Carla P. Gomes
openaire +4 more sources
Leida xuebao, 2022 The direct position determination method based on compressed sensing depends on the accurate signal propagation model. With partially unknown propagation model parameters, its location performance will decline significantly.
Hongzhen YE +4 more
doaj +1 more source
Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM [PDF]
, 2014 We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known.
Se Un Parka +5 more
core +1 more source
Correlated Sparse Bayesian Learning for Recovery of Block Sparse Signals With Unknown Borders
IEEE Open Journal of Signal ProcessingWe consider the problem of recovering complex-valued block sparse signals with unknown borders. Such signals arise naturally in numerous applications. Several algorithms have been developed to solve the problem of unknown block partitions.
Didem Dogan, Geert Leus
doaj +1 more source