Mar 7, 2021 · Low-rank matrices are pervasive throughout statistics, machine learning, signal processing, optimization, and applied mathematics. In this paper ...
Jun 6, 2023 · This paper establishes a novel Euclidean representation framework for low-rank matrices and provides a collection of theoretical and technical ...
Feb 15, 2023 · This paper establishes a novel Euclidean representation framework for low-rank matrices and provides a collection of theoretical and technical ...
Low-rank matrices are pervasive throughout statistics, machine learning, signal processing, optimization, and applied mathematics. In this paper, we propose ...
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Low-Rank Representation - an overview | ScienceDirect Topics
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Low-Rank Representation refers to a minimization problem that involves fitting a given data matrix to an approximating matrix with a low rank, ...
Jun 10, 2010 · Abstract—The low-rank matrix completion problem can be succinctly stated as follows: given a subset of the entries of a.
The main result of this thesis is the development of a theory of semidefinite facial reduction for the Euclidean distance matrix completion problem.
Jan 22, 2020 · The NSLRG method can learn the lowest rank matrix to satisfactorily represent the gene expression data and can capture the global structures and ...
In this paper, we formulate the Euclidean distance geometry (EDG) problem as a low rank matrix recovery problem. Adopting the matrix completion framework, our ...
Apr 4, 2020 · In this chapter we present numerical methods for low-rank matrix and tensor problems that explicitly make use of the geometry of rank ...