Multidimensional CX Decomposition of Tensors

The decomposition in terms of column space is used to reduce complexity and preserve the initial information contained in the data tensor. In this paper, we propose a multidimensional column-space decomposition to perform low rank approximation of tensors based on the CX decomposition for matrices....

Full description

Saved in:
Bibliographic Details
Published in2019 Workshop on Communication Networks and Power Systems (WCNPS) pp. 1 - 4
Main Authors Couras, Maria F. K. B., de Pinho, Pablo H., Favier, Gerard, da Costa, Joao P. C. L., Zarzoso, Vicente, de Almeida, Andre L. F.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2019
Subjects
Approximation algorithms
column space
Communication networks
Complexity theory
Conferences
Costs
low rank approximation
Matrix decomposition
Monte Carlo methods
Multidimensional CX Decomposition
Power systems
Tensors
Online AccessGet full text

Cover

More Information
Summary:The decomposition in terms of column space is used to reduce complexity and preserve the initial information contained in the data tensor. In this paper, we propose a multidimensional column-space decomposition to perform low rank approximation of tensors based on the CX decomposition for matrices. An algorithm is also presented to perform the approximation of the tensor based on the l 2 -norm. Monte Carlo simulation results are provided to illustrate the performance of the proposed CX-tensor decomposition and the associated algorithm.
DOI:10.1109/WCNPS.2019.8896279