Online dominant generalized eigenvectors extraction via a randomized method

Haoyuan Cai, Maboud F. Kaloorazi, Jie Chen, Wei Chen, Cédric Richard

科研成果: 书/报告/会议事项章节会议稿件同行评审

6 引用 (Scopus)

摘要

The generalized Hermitian eigendecomposition problem is ubiquitous in signal and machine learning applications. Considering the need of processing streaming data in practice and restrictions of existing methods, this paper is concerned with fast and efficient generalized eigenvectors tracking. We first present a computationally efficient algorithm based on randomization termed alternate-projections randomized eigenvalue decomposition (APR-EVD) to solve a standard eigenvalue problem. By exploiting rank-1 strategy, two online algorithms based on APR-EVD are developed for the dominant generalized eigenvectors extraction. Numerical examples show the practical applicability and efficacy of the proposed online algorithms.

源语言英语
主期刊名28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
出版商European Signal Processing Conference, EUSIPCO
2353-2357
页数5
ISBN(电子版)9789082797053
DOI
出版状态已出版 - 24 1月 2021
活动28th European Signal Processing Conference, EUSIPCO 2020 - Amsterdam, 荷兰
期限: 24 8月 202028 8月 2020

出版系列

姓名European Signal Processing Conference
2021-January
ISSN(印刷版)2219-5491

会议

会议28th European Signal Processing Conference, EUSIPCO 2020
国家/地区荷兰
Amsterdam
时期24/08/2028/08/20

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