Practical sketching algorithms for low-rank matrix approximation

31 August 2016

Papers citing "Practical sketching algorithms for low-rank matrix approximation"

8 / 8 papers shown

Title
SketchOGD: Memory-Efficient Continual Learning Benjamin Wright Youngjae Min Jeremy Bernstein Navid Azizan CLL 157 0 0 25 May 2023
Sparse PCA With Multiple Components Ryan Cory-Wright J. Pauphilet 140 2 0 29 Sep 2022
ISLET: Fast and Optimal Low-rank Tensor Regression via Importance Sketching Anru R. Zhang Yuetian Luo Garvesh Raskutti M. Yuan 153 44 0 09 Nov 2019
Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage A. Yurtsever Madeleine Udell J. Tropp Volkan Cevher 43 97 0 22 Feb 2017
Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm Prateek Jain Chi Jin Sham Kakade Praneeth Netrapalli Aaron Sidford 50 128 0 22 Feb 2016
Optimal approximate matrix product in terms of stable rank Michael B. Cohen Jelani Nelson David P. Woodruff 57 132 0 08 Jul 2015
Dimensionality Reduction for k-Means Clustering and Low Rank Approximation Michael B. Cohen Sam Elder Cameron Musco Christopher Musco Madalina Persu 111 358 0 24 Oct 2014
Toward a unified theory of sparse dimensionality reduction in Euclidean space J. Bourgain S. Dirksen Jelani Nelson 62 126 0 11 Nov 2013