A Majorization-Minimization Gauss-Newton Method for 1-Bit Matrix Completion.

ABSTRACT: In 1-bit matrix completion, the aim is to estimate an underlying low-rank matrix from a partial set of binary observations. We propose a novel method for 1-bit matrix completion called Majorization-Minimization Gauss-Newton (MMGN). Our method is based on the majorization-minimization principle, which converts the original optimization problem into a sequence of standard low-rank matrix completion problems. We solve each of these subproblems by a factorization approach that explicitly enforces the assumed low-rank structure and then apply a Gauss-Newton method. Using simulations and a real data example, we illustrate that in comparison to existing 1-bit matrix completion methods, MMGN outputs comparable if not more accurate estimates. In addition, it is often significantly faster, and less

SUBMITTER: Liu X 

PROVIDER: S-EPMC12327443 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature