Randomized Rank-Revealing QLP for Low-Rank Matrix Decomposition-Reference-Cited by-同舟云学术

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Randomized Rank-Revealing QLP for Low-Rank Matrix Decomposition

Published:2023 Issue: Volume:11 Page:63650-63666
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Kaloorazi Maboud F.¹^ORCID,Liu Kai²^ORCID,Chen Jie²,de Lamare Rodrigo C.³^ORCID,Rahardja Susanto²

Affiliation:

1. School of Electronic Engineering, Xi’an Shiyou University, Xi’an, China

2. School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, China

3. Center for Telecommunications Studies, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil

Funder

School of Electronic Engineering, Xi’an Shiyou University

NSFC

CNPq and FAPERJ

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10160001.pdf?arnumber=10160001

Reference58 articles.

1. Sketching for Principal Component Regression

2. Matrix Mathematics

3. Extreme-Scale Task-Based Cholesky Factorization Toward Climate and Weather Prediction Applications

4. Matrix Analysis

5. Hybrid Projection Methods with Recycling for Inverse Problems

Cited by 2 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. Randomized two-sided subspace iteration for low-rank matrix and tensor decomposition;Digital Signal Processing;2024-06

2. Online Learning Over Distributed Low-Rank Networks Via Sequential Power Iteration;2024

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