Gradient Descent Ascent for Minimax Problems on Riemannian Manifolds
Author:
Affiliation:
1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
2. Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software
Link
http://xplorestaging.ieee.org/ielx7/34/4359286/10005847.pdf?arnumber=10005847
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1. A unified framework for structured low-rank matrix learning;jawanpuria;Proc Int Conf Mach Learn,2018
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4. A Novel Riemannian Metric Based on Riemannian Structure and Scaling Information for Fixed Low-Rank Matrix Completion
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