ArticleSIAM journal on mathematics of data science2022
GNMR: A Provable One-Line Algorithm for Low Rank Matrix Recovery.
Article in SIAM journal on mathematics of data science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- A Majorization-Minimization Gauss-Newton Method for 1-Bit Matrix Completion.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- Missing Value Imputation With Low-Rank Matrix Completion in Single-Cell RNA-Seq Data by Considering Cell Heterogeneity.Frontiers in genetics · 2022Article
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Abstract
Low rank matrix recovery problems appear in a broad range of applications. In this work we present GNMR-an extremely simple iterative algorithm for low rank matrix recovery, based on a Gauss-Newton linearization. On the theoretical front, we derive recovery guarantees for GNMR in both matrix sensing and matrix completion settings. Some of these results improve upon the best currently known for other methods. A key property of GNMR is that it implicitly keeps the factor matrices approximately balanced throughout its iterations. On the empirical front, we show that for matrix completion with uniform sampling, GNMR performs better than several popular methods, especially when given very few observations close to the information limit.
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