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Low-rank tensor completion: a Riemannian manifold preconditioning
  approach

Low-rank tensor completion: a Riemannian manifold preconditioning approach

26 May 2016
Hiroyuki Kasai
Bamdev Mishra
ArXivPDFHTML

Papers citing "Low-rank tensor completion: a Riemannian manifold preconditioning approach"

15 / 15 papers shown
Title
Provably Accelerating Ill-Conditioned Low-rank Estimation via Scaled
  Gradient Descent, Even with Overparameterization
Provably Accelerating Ill-Conditioned Low-rank Estimation via Scaled Gradient Descent, Even with Overparameterization
Cong Ma
Xingyu Xu
Tian Tong
Yuejie Chi
18
9
0
09 Oct 2023
Nonnegative Low-Rank Tensor Completion via Dual Formulation with
  Applications to Image and Video Completion
Nonnegative Low-Rank Tensor Completion via Dual Formulation with Applications to Image and Video Completion
Tanmay Sinha
Jayadev Naram
Pawan Kumar
36
10
0
13 May 2023
Structured Low-Rank Tensor Learning
Structured Low-Rank Tensor Learning
Jayadev Naram
Tanmay Sinha
Pawan Kumar
26
1
0
13 May 2023
Subquadratic Kronecker Regression with Applications to Tensor
  Decomposition
Subquadratic Kronecker Regression with Applications to Tensor Decomposition
Matthew Fahrbach
Thomas Fu
Mehrdad Ghadiri
35
15
0
11 Sep 2022
Partial Least Square Regression via Three-factor SVD-type Manifold
  Optimization for EEG Decoding
Partial Least Square Regression via Three-factor SVD-type Manifold Optimization for EEG Decoding
Wanguang Yin
Zhichao Liang
Jianguo Zhang
Quanying Liu
18
3
0
09 Aug 2022
Tensor-on-Tensor Regression: Riemannian Optimization,
  Over-parameterization, Statistical-computational Gap, and Their Interplay
Tensor-on-Tensor Regression: Riemannian Optimization, Over-parameterization, Statistical-computational Gap, and Their Interplay
Yuetian Luo
Anru R. Zhang
33
19
0
17 Jun 2022
Operator-valued formulas for Riemannian Gradient and Hessian and
  families of tractable metrics
Operator-valued formulas for Riemannian Gradient and Hessian and families of tractable metrics
Du Nguyen
16
5
0
21 Sep 2020
Tensor Q-Rank: New Data Dependent Definition of Tensor Rank
Tensor Q-Rank: New Data Dependent Definition of Tensor Rank
Hao Kong
Canyi Lu
Zhouchen Lin
32
36
0
26 Oct 2019
McTorch, a manifold optimization library for deep learning
McTorch, a manifold optimization library for deep learning
Mayank Meghwanshi
Pratik Jawanpuria
Anoop Kunchukuttan
Hiroyuki Kasai
Bamdev Mishra
AI4CE
28
41
0
03 Oct 2018
Brain-Computer Interface with Corrupted EEG Data: A Tensor Completion
  Approach
Brain-Computer Interface with Corrupted EEG Data: A Tensor Completion Approach
Jordi Solé-Casals
C. Caiafa
Qibin Zhao
A. Cichocki
18
33
0
13 Jun 2018
Inductive Framework for Multi-Aspect Streaming Tensor Completion with
  Side Information
Inductive Framework for Multi-Aspect Streaming Tensor Completion with Side Information
M. Nimishakavi
Bamdev Mishra
Manish Gupta
Partha P. Talukdar
24
22
0
18 Feb 2018
Tensor Completion Algorithms in Big Data Analytics
Tensor Completion Algorithms in Big Data Analytics
Qingquan Song
Hancheng Ge
James Caverlee
Xia Hu
31
229
0
28 Nov 2017
Riemannian Tensor Completion with Side Information
Riemannian Tensor Completion with Side Information
Tengfei Zhou
Hui Qian
Zebang Shen
Congfu Xu
19
4
0
12 Nov 2016
Low-tubal-rank Tensor Completion using Alternating Minimization
Low-tubal-rank Tensor Completion using Alternating Minimization
Xiao-Yang Liu
Shuchin Aeron
Vaneet Aggarwal
Xiaodong Wang
22
119
0
05 Oct 2016
Riemannian preconditioning for tensor completion
Riemannian preconditioning for tensor completion
Hiroyuki Kasai
Bamdev Mishra
19
10
0
06 Jun 2015
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