scholarly journals Low-Rank Matrix Approximations Do Not Need a Singular Value Gap

2019 ◽  
Vol 40 (1) ◽  
pp. 299-319 ◽  
Author(s):  
Petros Drineas ◽  
Ilse C. F. Ipsen
Keyword(s):  
Singular Value ◽  
Low Rank ◽  
Rank Matrix ◽  
Low Rank Matrix ◽  
Matrix Approximations

scholarly journals A Singular Value Thresholding with Diagonal-Update Algorithm for Low-Rank Matrix Completion

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yong-Hong Duan ◽  
Rui-Ping Wen ◽  
Yun Xiao
Keyword(s):  
Matrix Completion ◽  
Arithmetic Operation ◽  
Computational Cost ◽  
Singular Value ◽  
Low Rank ◽  
Simple Arithmetic ◽  
Rank Matrix ◽  
Low Rank Matrix Completion ◽  
Low Rank Matrix ◽  
Singular Value Thresholding

The singular value thresholding (SVT) algorithm plays an important role in the well-known matrix reconstruction problem, and it has many applications in computer vision and recommendation systems. In this paper, an SVT with diagonal-update (D-SVT) algorithm was put forward, which allows the algorithm to make use of simple arithmetic operation and keep the computational cost of each iteration low. The low-rank matrix would be reconstructed well. The convergence of the new algorithm was discussed in detail. Finally, the numerical experiments show the effectiveness of the new algorithm for low-rank matrix completion.

scholarly journals Subspace-Orbit Randomized Decomposition for Low-Rank Matrix Approximations

2018 ◽  
Vol 66 (16) ◽  
pp. 4409-4424 ◽  
Author(s):  
Maboud Farzaneh Kaloorazi ◽  
Rodrigo C. de Lamare
Keyword(s):  
Low Rank ◽  
Rank Matrix ◽  
Low Rank Matrix ◽  
Matrix Approximations

Low-rank matrix approximations for Coherent point drift

2015 ◽  
Vol 52 ◽  
pp. 53-58 ◽  
Author(s):  
Ján Dupej ◽  
Václav Krajíček ◽  
Josef Pelikán
Keyword(s):  
Low Rank ◽  
Rank Matrix ◽  
Coherent Point Drift ◽  
Low Rank Matrix ◽  
Matrix Approximations
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