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Communication Dans Un Congrès Année : 2021

Online Dominant Generalized Eigenvectors Extraction via a Randomized Method

Haoyuan Cai
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Jie Chen
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Wei Chen
Cédric Richard

Résumé

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.
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Dates et versions

hal-03347376 , version 1 (17-09-2021)

Identifiants

Citer

Haoyuan Cai, Maboud F. Kaloorazi, Jie Chen, Wei Chen, Cédric Richard. Online Dominant Generalized Eigenvectors Extraction via a Randomized Method. 2020 28th European Signal Processing Conference (EUSIPCO), Jan 2021, Amsterdam (virtual), France. pp.2353-2357, ⟨10.23919/Eusipco47968.2020.9287345⟩. ⟨hal-03347376⟩
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