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Adaptive stochastic gradient descent on the Grassmannian for robust low‐rank subspace recovery

  • Published:2016-10 Issue:8 Volume:10 Page:1000-1008
  • ISSN:1751-9675
  • Container-title:IET Signal Processing
  • language:en
  • Short-container-title:IET signal process.

Author:

He Jun1,Zhang Yue2,Zhou Yuan1,Zhang Lei3

Affiliation:

1. School of Electronic and Information EngineeringNanjing University of Information Science and Technology210044People's Republic of China

2. Department of Computer ScienceState University of New York Binghamton UniversityUSA

3. School of Electrical and Automation EngineeringNanjing Normal University210042People's Republic of China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Signal Processing

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. RelEx: A Model-Agnostic Relational Model Explainer;Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society;2021-07-21

2. A partial sum of singular‐value‐based reconstruction method for non‐uniformly sampled NMR spectroscopy;IET Signal Processing;2020-12-24

3. LMSK: a robust higher‐order gradient‐based adaptive algorithm;IET Signal Processing;2019-07

4. A deep learning approach for detecting candidates of supernova remnants;Research in Astronomy and Astrophysics;2019-03
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