Rank, decomposition, and uniqueness for 3-way and n-way arrays
Multiway data analysis
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Rank-One Approximation to High Order Tensors
SIAM Journal on Matrix Analysis and Applications
SIAM Journal on Matrix Analysis and Applications
MATH'07 Proceedings of the 12th WSEAS International Conference on Applied Mathematics
Fast Multilinear Singular Value Decomposition for Structured Tensors
SIAM Journal on Matrix Analysis and Applications
A Jacobi-Type Method for Computing Orthogonal Tensor Decompositions
SIAM Journal on Matrix Analysis and Applications
Tensor Decompositions and Applications
SIAM Review
Breaking the Curse of Dimensionality, Or How to Use SVD in Many Dimensions
SIAM Journal on Scientific Computing
Approximation of $2^d\times2^d$ Matrices Using Tensor Decomposition
SIAM Journal on Matrix Analysis and Applications
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In this study, very early steps of a new algorithm for multiway array decomposition via High Dimensional Model Representation (HDMR) is proposed. HDMR is originally developed for the multivariate functions and represents them as the sum of lower variate functions inluding the constant term, and thus HDMR is an inherent candidate for decomposing multiway arrays. The proposed algorithm represents given multiway array valued multivariate function as the sum of same type multiway array valued functions with lower multivariances starting from the constancy in ascending multivariance. This algorithm generalizes and unifies the recently proposed algorithms Vector HDMR and Matrix HDMR by Demiralp and his group and thus enlights the big picture of a new family of multiway array decomposition algorithms based on High Dimensional Model Representation.