Finding Low-Rank Solutions via Non-Convex Matrix Factorization,
Efficiently and Provably

Finding Low-Rank Solutions via Non-Convex Matrix Factorization, Efficiently and Provably

10 June 2016

Anastasios Kyrillidis

Sujay Sanghavi

Papers citing "Finding Low-Rank Solutions via Non-Convex Matrix Factorization, Efficiently and Provably"

7 / 7 papers shown

Title
Rank-One Measurements of Low-Rank PSD Matrices Have Small Feasible Sets T. Roddenberry Santiago Segarra Anastasios Kyrillidis 21 0 0 17 Dec 2020
Provably convergent acceleration in factored gradient descent with applications in matrix sensing Tayo Ajayi David Mildebrath Anastasios Kyrillidis Shashanka Ubaru Georgios Kollias K. Bouchard 18 1 0 01 Jun 2018
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow Xiao Zhang S. Du Quanquan Gu 26 24 0 03 Mar 2018
Robust Kronecker-Decomposable Component Analysis for Low-Rank Modeling Mehdi Bahri Yannis Panagakis S. Zafeiriou 37 14 0 22 Mar 2017
A Unified Computational and Statistical Framework for Nonconvex Low-Rank Matrix Estimation Lingxiao Wang Xiao Zhang Quanquan Gu 16 80 0 17 Oct 2016
Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach Dohyung Park Anastasios Kyrillidis C. Caramanis Sujay Sanghavi 23 179 0 12 Sep 2016
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset T. Bouwmans A. Sobral S. Javed Soon Ki Jung E. Zahzah 39 330 0 04 Nov 2015