tSVDdct: Singular value decomposition (SVD) of a 3D tensor using the...

In TensorTools: Multilinear Algebra

Singular value decomposition (SVD) of a 3D tensor using the discrete cosine transform

Description

Singular value decomposition (SVD) of a 3D tensor using the discrete cosine transform

Usage

tSVDdct(tnsr)

Arguments

tnsr	a 3-mode S3 tensor class object

Value

U, the left singular value tensor object (m x m x k)

V, The right singular value tensor object (n x n x k)

S: A diagonal tensor (m x n x k)#' @examples V: The right singular value tensor object (n x n x k) S: A diagonal tensor (m x n x k)

Author(s)

Kyle Caudle

Randy Hoover

Jackson Cates

Everett Sandbo

References

M. E. Kilmer, C. D. Martin, and L. Perrone, “A third-order generalization of the matrix svd as a product of third-order tensors,” Tufts University, Department of Computer Science, Tech. Rep. TR-2008-4, 2008

K. Braman, "Third-order tensors as linear operators on a space of matrices", Linear Algebra and its Applications, vol. 433, no. 7, pp. 1241-1253, 2010.

Examples

T <- t_rand(modes=c(2,2,4))
tSVDdct(T)

TensorTools documentation built on Oct. 18, 2024, 1:07 a.m.