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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Bibliographic Details
Main Authors: ZHOU, Pan, YUAN, Xiao-Tong, YAN, Shuicheng, FENG, Jiashi
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2019
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Online Access:https://ink.library.smu.edu.sg/sis_research/8990
https://ink.library.smu.edu.sg/context/sis_research/article/9993/viewcontent/2019_TPAMI_manifold.pdf
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Institution: Singapore Management University
Language: English
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