Enhanced Low-Rank Matrix Approximation

doi:doi:10.57702/0hw3unje

Enhanced Low-Rank Matrix Approximation

This letter proposes to estimate low-rank matrices by formulating a convex optimization problem with non-convex regularization. We employ parameterized non-convex penalty functions to estimate the non-zero singular values more accurately than the nuclear norm.

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Ankit Parekh, Ivan W. Selesnick (2024). Dataset: Enhanced Low-Rank Matrix Approximation. https://doi.org/10.57702/0hw3unje

DOI retrieved: December 3, 2024

Additional Info

Field	Value
Created	December 3, 2024
Last update	December 3, 2024
Defined In	https://doi.org/10.1109/LSP.2016.2535227
Author	Ankit Parekh
More Authors	Ivan W. Selesnick