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Given A ∈ C n × n , a low-rank matrix approximation problem [20] aims at finding an approximant E of A such that the rank of E is upper bounded by some given r ≥ 0 and such that the distance between E and A is as small as possible.
Apr 15, 2022
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Nov 30, 2020 · Abstract:In this paper, we propose and solve a low phase-rank approximation problem, which serves as a counterpart to the well-known ...
In mathematics, low-rank approximation refers to the process of approximating a given matrix by a matrix of lower rank.
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Apr 15, 2022 · In this paper, we propose and solve low phase-rank approximation problems, which serve as a counterpart to the well-known low-rank approximation problem and ...
Apr 19, 2024 · Key Idea 1 (Low-Rank Approximation). Low-rank approximation of a matrix A ∈ Rm×n with rank r is the process of finding another matrix. Aℓ ∈ Rm×n ...
Apr 15, 2023 · Consequently such an approximation gives only unique results if complete blocks of multiple singular values are cut away, that is, if σrk+1<σrk.
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Dec 1, 2020 · Abstract. In this paper, we propose and solve a low phase-rank approximation problem, which serves as a counterpart to the well-known ...
Aug 8, 2022 · Principal component analysis is the data analysis technique that comes from using the SVD-based low rank approximation to the matrix of interest.
Low-rank matrix approximations are essential tools in the application of kernel methods to large-scale learning problems.
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Low-rank approximation is useful in large data analysis, especially in predicting missing entries of a matrix by projecting the row and column entities (e.g., ...