Matrix completion with nonconvex regularization: spectral operators and scalable algorithms
Abstract In this paper, we study the popularly dubbed matrix completion problem, where the task is to “fill in” the unobserved entries of a matrix from a small subset of observed entries, under the assumption that the underlying matrix is of low rank. Our contributions herein enhance our prior work...
Main Authors: | Mazumder, Rahul, Saldana, Diego, Weng, Haolei |
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Other Authors: | Sloan School of Management |
Format: | Article |
Language: | English |
Published: |
Springer US
2021
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Online Access: | https://hdl.handle.net/1721.1/131497 |
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