TensorTools (version 1.0.0)

tSVDdwht: Singular value decomposition (SVD) of a 3D tensor using the discrete Walsh Hadley transform

Description

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

Usage

tSVDdwht(tnsr)

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 \(k\))

Arguments

tnsr,

a 3-mode S3 tensor class object

Author

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))
tSVDdwht(T)