Towards Efficient and Accurate Approximation: Tensor Decomposition Based on Randomized Block Krylov Iteration

27 November 2022

Papers citing "Towards Efficient and Accurate Approximation: Tensor Decomposition Based on Randomized Block Krylov Iteration"

11 / 11 papers shown

Title
$Low-Rank Approximation with $1/ε^{1/3}$ Matrix-Vector Products$ Low-Rank Approximation with $1/ε^{1/3}$ Matrix-Vector Products Ainesh Bakshi K. Clarkson David P. Woodruff 57 16 0 10 Feb 2022
More Efficient Sampling for Tensor Decomposition With Worst-Case Guarantees Osman Asif Malik 63 15 0 14 Oct 2021
An Improved Frequent Directions Algorithm for Low-Rank Approximation via Block Krylov Iteration Chenhao Wang Qianxin Yi Xiuwu Liao Yao Wang 53 2 0 24 Sep 2021
Fast and Accurate Randomized Algorithms for Low-rank Tensor Decompositions Linjian Ma Edgar Solomonik 45 26 0 02 Apr 2021
Tucker decomposition-based Temporal Knowledge Graph Completion Pengpeng Shao Guohua Yang Dawei Zhang J. Tao Feihu Che Tong Liu 76 84 0 16 Nov 2020
Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction Longhao Yuan Chong Li Jianting Cao Qibin Zhao 35 26 0 07 Jan 2019
Randomized CP Tensor Decomposition N. Benjamin Erichson Krithika Manohar Steven L. Brunton J. Nathan Kutz 63 64 0 27 Mar 2017
Practical sketching algorithms for low-rank matrix approximation J. Tropp A. Yurtsever Madeleine Udell Volkan Cevher 117 209 0 31 Aug 2016
Tensor Ring Decomposition Qibin Zhao Guoxu Zhou Shengli Xie Liqing Zhang A. Cichocki 66 360 0 17 Jun 2016
Fast and Guaranteed Tensor Decomposition via Sketching Yining Wang H. Tung Alex Smola Anima Anandkumar 122 130 0 14 Jun 2015
A Practical Guide to Randomized Matrix Computations with MATLAB Implementations Shusen Wang 64 39 0 28 May 2015