Low-Rank Matrix Completion via QR-Based Retraction on Manifolds
نویسندگان
چکیده
Low-rank matrix completion aims to recover an unknown from a subset of observed entries. In this paper, we solve the problem via optimization manifold. Specially, apply QR factorization retraction during optimization. We devise two fast algorithms based on steepest gradient descent and conjugate descent, demonstrate their superiority over promising baseline with ratio at least 24%.
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Low-Rank Matrix Completion
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ژورنال
عنوان ژورنال: Mathematics
سال: 2023
ISSN: ['2227-7390']
DOI: https://doi.org/10.3390/math11051155