On the Optimal Tradeoff Between Computational Efficiency and Generalizability of Oja’s Algorithm
The Oja's algorithm is widely applied for computing principal eigenvectors in real problems, and it is practically useful to understand the theoretical relationships between the learning rate, convergence rate, and generalization error of this algorithm for noisy samples. In this paper, we show...
| Principais autores: | , |
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| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
IEEE
2020-01-01
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| coleção: | IEEE Access |
| Assuntos: | |
| Acesso em linha: | https://ieeexplore.ieee.org/document/9104701/ |