Nov 3, 2016 · This error measure is known to be more robust than the Frobenius norm in the presence of outliers and is indicated in models where Gaussian ...
We give the first provable approximation algorithms for ℓ1-low rank approximation, showing that it is possible to achieve approximation factor α = (logd) #183; ...
Apr 16, 2020 · We give a number of additional results for `1-low rank approximation: nearly tight upper and lower bounds for column subset selection, CUR ...
It is more robust than the Frobenius norm in the presence of outliers. It is indicated in models where Gaussian assumptions on the noise may not apply.
Oct 25, 2013 · Low-rank matrix approximation in terms of entry-wise L1 norm ... is given by the SVD of A, where ‖⋅‖F denotes the Frobenius norm.
The first provable approximation algorithms for ℓ1-low rank approximation are given, showing that it is possible to achieve approximation factor α = (logd) ...
Unlike Frobenius norm low rank approximation, which can be solved exactly using the singular value decomposition, no such algorithm or closed-form solution is ...
This error measure is known to be more robust than the Frobenius norm in the presence of outliers and is indicated in models where Gaussian assumptions on the ...
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Nov 3, 2016 · The first provable approximation algorithms for $\ell_1$-low rank approximation are given, showing that it is possible to achieve ...
In mathematics, low-rank approximation refers to the process of approximating a given matrix by a matrix of lower rank.