Our algorithms are simple, easy to implement, work well in practice, and illustrate interesting tradeoffs between the approximation quality, the running time, ...
May 18, 2017 · Our algorithms are simple, easy to implement, work well in practice, and illustrate interesting tradeoffs between the approximation quality, the ...
These algorithms are simple, easy to implement, work well in practice, and illustrate interesting tradeoffs between the approximation quality, ...
Jul 16, 2018 · A number of recent works have studied algorithms for entrywise \ell_p-low rank approximation, namely, algorithms which given an n \times d matrix A (with n \ ...
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., ...
Missing: ell_p$ | Show results with:ell_p$
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We study the ℓ0 ℓ 0 -Low Rank Approximation Problem, where the goal is, given an m×n m × n matrix A A , to output a rank-k k matrix A′ A ′ for which ∥A′−A∥0 ‖ A ...
Missing: ell_p$ | Show results with:ell_p$
Our algorithms are simple, easy to implement, work well in practice, and illustrate interesting tradeoffs between the approximation quality, the running time, ...
Missing: ell_p$ | Show results with:ell_p$
Sep 12, 2024 · Our algorithms are simple, easy to implement, work well in practice, and illustrate interesting tradeoffs between the approximation quality, the ...
We study the `0-Low Rank Approximation Problem, where the goal is, given an m × n matrix A, to output a rank-k matrix A0 for which kA0 − Ak0 is minimized.
May 5, 2023 · For example, to make the algorithm of column subset selection computationally efficient, we analyze a polynomial time bi-criteria algorithm ...