On unique tensor rank decomposition of 3-tensors

被引:1
|
作者
Gubkin, Pavel [1 ,2 ,3 ,4 ]
机构
[1] St Petersburg State Univ, St Petersburg, Russia
[2] Russian Acad Sci, St Petersburg Dept, Steklov Math Inst, St Petersburg, Russia
[3] St Petersburg State Univ, Univ Skaya nab 7-9, St Petersburg 199034, Russia
[4] Russian Acad Sci, St Petersburg Dept, Steklov Math Inst, Fontanka 27, St Petersburg 191023, Russia
来源
LINEAR & MULTILINEAR ALGEBRA | 2024年 / 72卷 / 11期
关键词
Kruskal theorem; tensor rank; tensor decomposition; CANONICAL POLYADIC DECOMPOSITION;
D O I
10.1080/03081087.2023.2211718
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
We answer to a question posed recently in reference [Lovitz B, Petrov F. A generalization of Kruskal's theorem on tensor decomposition. Available at arXiv 2103.15633; 2021], proving the conjectured sufficient minimality and uniqueness condition of the 3-tensor decomposition.
引用
收藏
页码:1860 / 1866
页数:7
相关论文
共 50 条
  • [41] CP DECOMPOSITION AND LOW-RANK APPROXIMATION OF ANTISYMMETRIC TENSORS
    Kovac, Erna Begovic
    Perisa, Lana
    ELECTRONIC TRANSACTIONS ON NUMERICAL ANALYSIS, 2024, 62 : 72 - 94
  • [42] Under-Determined Tensor Diagonalization for Decomposition of Difficult Tensors
    Tichavsky, Petr
    Phan, Anh-Huy
    Cichocki, Andrzej
    2017 IEEE 7TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING (CAMSAP), 2017,
  • [43] Tensor Denoising Using Low-Rank Tensor Train Decomposition
    Gong, Xiao
    Chen, Wei
    Chen, Jie
    Ai, Bo
    IEEE SIGNAL PROCESSING LETTERS, 2020, 27 : 1685 - 1689
  • [44] Rank minimization on tensor ring: an efficient approach for tensor decomposition and completion
    Yuan, Longhao
    Li, Chao
    Cao, Jianting
    Zhao, Qibin
    MACHINE LEARNING, 2020, 109 (03) : 603 - 622
  • [45] Rank minimization on tensor ring: an efficient approach for tensor decomposition and completion
    Longhao Yuan
    Chao Li
    Jianting Cao
    Qibin Zhao
    Machine Learning, 2020, 109 : 603 - 622
  • [46] Unique Sparse Decomposition of Low Rank Matrices
    Jin, Dian
    Bing, Xin
    Zhang, Yuqian
    IEEE TRANSACTIONS ON INFORMATION THEORY, 2023, 69 (04) : 2452 - 2484
  • [47] Unique sparse decomposition of low rank matrices
    Jin, Dian
    Bing, Xin
    Zhang, Yuqian
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021), 2021,
  • [48] Video Denoising Using Low Rank Tensor Decomposition
    Gui, Lihua
    Cui, Gaochao
    Zhao, Qibin
    Wang, Dongsheng
    Cichocki, Andrzej
    Cao, Jianting
    NINTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2016), 2017, 10341
  • [49] Statistical mechanics of low-rank tensor decomposition
    Kadmon, Jonathan
    Ganguli, Surya
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), 2018, 31
  • [50] Statistical mechanics of low-rank tensor decomposition
    Kadmon, Jonathan
    Ganguli, Surya
    JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, 2019, 2019 (12):