A Gradient System for Low Rank Matrix Completion
AbstractIn this article we present and discuss a two step methodology to find the closest low rank completion of a sparse large matrix. Given a large sparse matrix M, the method consists of fixing the rank to r and then looking for the closest rank-r matrix X to M, where the distance is measured in the Frobenius norm. A key element in the solution of this matrix nearness problem consists of the use of a constrained gradient system of matrix differential equations. The obtained results, compared to those obtained by different approaches show that the method has a correct behaviour and is competitive with the ones available in the literature. View Full-Text
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Scalone, C.; Guglielmi, N. A Gradient System for Low Rank Matrix Completion. Axioms 2018, 7, 51.
Scalone C, Guglielmi N. A Gradient System for Low Rank Matrix Completion. Axioms. 2018; 7(3):51.Chicago/Turabian Style
Scalone, Carmela; Guglielmi, Nicola. 2018. "A Gradient System for Low Rank Matrix Completion." Axioms 7, no. 3: 51.
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