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- W3114713599 abstract "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." @default.
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- W3114713599 date "2021-01-24" @default.
- W3114713599 modified "2023-09-24" @default.
- W3114713599 title "Online Dominant Generalized Eigenvectors Extraction Via A Randomized Method" @default.
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- W3114713599 doi "https://doi.org/10.23919/eusipco47968.2020.9287345" @default.
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