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2015 International Conference on Big Data and Smart Computing (BigComp) (2015)
Jeju, South Korea
Feb. 9, 2015 to Feb. 11, 2015
ISBN: 978-1-4799-7303-3
pp: 11-14
Jun Li , Department of Electronics and Information Engineering, Chonbuk National University, South Korea
Yier Yan , School of Mechanical and Electrical Engineering, Guangzhou University, China
Wei Duan , Department of Electronics and Information Engineering, Chonbuk National University, South Korea
Sangseob Song , Department of Electronics and Information Engineering, Chonbuk National University, South Korea
Moon Ho Lee , Department of Electronics and Information Engineering, Chonbuk National University, South Korea
ABSTRACT
In this paper, we consider the tensor decomposition (TD) of Toeplitz Jacket (TJ) matrices for big data processing by using the conventional higher order singular value decomposition (HOSVD) algorithm and Tensor train (TT) decomposition. In order to use HOSVD algorithm and TT decomposition, we reshape the given matrix and make it as a tensor. Due to the property of Toeplitz matrices, we use a truncated TJ matrix in stead of given matrix to reduce the complexity of TD. The results verified that the TD of the truncated TJ matrices gains a lower complexity due to smaller size of factor matrices and core tensors.
INDEX TERMS
Matrix decomposition, Tensile stress, Complexity theory, Big data, Singular value decomposition, Standards, Approximation methods
CITATION

J. Li, Y. Yan, W. Duan, S. Song and M. H. Lee, "Tensor decomposition of Toeplitz Jacket matrices for big data processing," 2015 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Jeju, South Korea, 2015, pp. 11-14.
doi:10.1109/35021BIGCOMP.2015.7072840
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