LRMC is a deep-unfolded non-convex algorithm for large-scale robust matrix completion that learns its parameters for linear convergence and better empirical results than prior methods.
Theoretical analysis on conver- gence acceleration of deep-unfolded gradient descent
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Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery
LRMC is a deep-unfolded non-convex algorithm for large-scale robust matrix completion that learns its parameters for linear convergence and better empirical results than prior methods.