Faster First-Order Methods for Stochastic Non-Convex Optimization on Riemannian Manifolds

First-order non-convex Riemannian optimization algorithms have gained recent popularity in structured machine learning problems including principal component analysis and low-rank matrix completion. The current paper presents an efficient Riemannian Stochastic Path Integrated Differential EstimatoR...

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Publié dans:	IEEE transactions on pattern analysis and machine intelligence. - 1979. - 43(2021), 2 vom: 26. Feb., Seite 459-472
Auteur principal:	Zhou, Pan (Auteur)
Autres auteurs:	Yuan, Xiao-Tong, Yan, Shuicheng, Feng, Jiashi
Format:	Article en ligne
Langue:	English
Publié:	2021
Accès à la collection:	IEEE transactions on pattern analysis and machine intelligence
Sujets:	Journal Article