TENSOR COMPLETION THROUGH MULTIPLE KRONECKER PRODUCT DECOMPOSITION

被引:0
|
作者
Anh-Huy Phan [1 ]
Cichocki, Andrzej [1 ]
Tichavsky, Petr [2 ]
Luta, Gheorghe [3 ]
Brockmeier, Austin [4 ]
机构
[1] RIKEN, Brain Sci Inst, Wako, Japan
[2] Inst Informat Theory & Automat, Prague, Czech Republic
[3] Georgetown Univ, Dept Biostat Bioinformat & Biomath, Washington, DC USA
[4] Univ Florida, Dept Elect & Comp Engn, Gainesville, FL USA
关键词
tensor decomposition; tensor completion; Kronecker tensor decomposition (KTD); color image; NONNEGATIVE MATRIX; THRESHOLDING ALGORITHM; RECONSTRUCTION;
D O I
暂无
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
We propose a novel decomposition approach to impute missing values in tensor data. The method uses smaller scale multiway patches to model the whole data or a small volume encompassing the observed missing entries. Simulations on color images show that our method can recover color images using only 5-10% of pixels, and outperforms other available tensor completion methods.
引用
收藏
页码:3233 / 3237
页数:5
相关论文
共 50 条
  • [1] Kronecker Product Approximation with Multiple Factor Matrices via the Tensor Product Algorithm
    Wu, King Keung
    Yam, Yeung
    Meng, Helen
    Mesbahi, Mehran
    2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2016, : 4277 - 4282
  • [2] Smooth Tensor Product for Tensor Completion
    Wu, Tongle
    Fan, Jicong
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2024, 33 : 6483 - 6496
  • [3] Convolutional Neural Network Compression through Generalized Kronecker Product Decomposition
    Hameed, Marawan Gamal Abdel
    Tahaei, Marzieh S.
    Mosleh, Ali
    Nia, Vahid Partovi
    THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / THE TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2022, : 771 - 779
  • [4] Subquadratic Kronecker Regression with Applications to Tensor Decomposition
    Fahrbach, Matthew
    Fu, Gang
    Ghadiri, Mehrdad
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 35 (NEURIPS 2022), 2022,
  • [5] Hierarchical Kronecker tensor-product approximations
    Hackbusch, W.
    Khoromskij, B.N.
    Tyrtyshnikov, E.E.
    Journal of Numerical Mathematics, 2005, 13 (02) : 119 - 156
  • [6] Hybrid Kronecker Product Decomposition and Approximation
    Cai, Chencheng
    Chen, Rong
    Xiao, Han
    JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2023, 32 (03) : 838 - 852
  • [7] A New Method of Kronecker Product Decomposition
    Wu, Yi
    JOURNAL OF MATHEMATICS, 2023, 2023
  • [8] Tensor Product Decomposition
    Kumar, Shrawan
    PROCEEDINGS OF THE INTERNATIONAL CONGRESS OF MATHEMATICIANS, VOL III: INVITED LECTURES, 2010, : 1226 - 1261
  • [9] Tensor Wheel Decomposition and Its Tensor Completion Application
    Wu, Zhong-Cheng
    Huang, Ting-Zhu
    Deng, Liang-Jian
    Dou, Hong-Xia
    Meng, Deyu
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 35 (NEURIPS 2022), 2022,
  • [10] Tensor Completion via the CP Decomposition
    Sanogo, Fatoumata
    Navasca, Carmeliza
    2018 CONFERENCE RECORD OF 52ND ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS, 2018, : 845 - 849