$On the Complexity of Robust PCA and $\ell_1$-norm Low-Rank Matrix Approximation$

On the Complexity of Robust PCA and $\ell_1$ -norm Low-Rank Matrix Approximation

30 September 2015

Papers citing "On the Complexity of Robust PCA and $\ell_1$-norm Low-Rank Matrix Approximation"

6 / 6 papers shown

Title
Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss Pierre Bréchet Katerina Papagiannouli Jing An Guido Montúfar 33 3 0 06 Mar 2023
Learning Green's functions associated with time-dependent partial differential equations N. Boullé Seick Kim Tianyi Shi Alex Townsend AI4CE 29 25 0 27 Apr 2022
An Overview of Robust Subspace Recovery Gilad Lerman Tyler Maunu 15 130 0 02 Mar 2018
$Approximation Algorithms for $\ell_0$-Low Rank Approximation$ Approximation Algorithms for $\ell_0$ -Low Rank Approximation K. Bringmann Pavel Kolev David P. Woodruff 24 13 0 30 Oct 2017
Recovery Guarantees for One-hidden-layer Neural Networks Kai Zhong Zhao Song Prateek Jain Peter L. Bartlett Inderjit S. Dhillon MLT 34 336 0 10 Jun 2017
$On Verifiable Sufficient Conditions for Sparse Signal Recovery via $\ell_1$ Minimization$ On Verifiable Sufficient Conditions for Sparse Signal Recovery via $\ell_1$ Minimization A. Juditsky A. Nemirovski 106 142 0 16 Sep 2008