Multi-Task Sparse Signal Recovery with Dirichlet Process Priors Based on Expectation Propagation Technique-Reference-Cited by-同舟云学术

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Multi-Task Sparse Signal Recovery with Dirichlet Process Priors Based on Expectation Propagation Technique

Published:2022-07-20 Issue: Volume: Page:
ISSN:
Container-title:2022 7th International Conference on Signal and Image Processing (ICSIP)
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Author:

Fu Yin¹,Wu Qisong¹,Zhang Yimin D.²,Amin Moeness G.³

Affiliation:

1. Southeast University,Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education,Nanjing,China,210096

2. Temple University,Department of Electrical and Computer Engineering,Philadelphia,PA,USA,19122

3. Villanova University,Center for Advanced Communications,Villanova,PA,USA,19085

Funder

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9886003/9885802/09886972.pdf?arnumber=9886972

Reference27 articles.

1. Compressive sensing meets time– frequency: an overview of recent advances in time–frequency processing of sparse signals;ervin;Digital Signal Processing,2018

2. On the sparsity bound for the existence of a unique solution in compressive sensing by the Gershgorin theorem

3. Multitask Sparse Nonnegative Matrix Factorization for Joint Spectral–Spatial Hyperspectral Imagery Denoising

4. Object tracking via robust multitask sparse representation;bai;IEEE Signal Processing Letters,2014

5. A framework for learning predictive structures from multiple tasks and unlabeled data;ando;Journal of Machine Learning Research,2005

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