tEIG: Tensor Eigenvalue Decomposition Using any Discrete Transform

In rTensor2: MultiLinear Algebra

Tensor Eigenvalue Decomposition Using any Discrete Transform

Description

Performs a Eigenvalue decomposition of 3-mode tensor using any discrete transform.

Usage

tEIG(tnsr, tform)

Arguments

tnsr	: a 3-mode tensor, n x n x k
tform	: Any discrete transform. Supported transforms are: fft: Fast Fourier Transform dwt: Discrete Wavelet Transform (Haar Wavelet) dct: Discrete Cosine transform dst: Discrete Sine transform dht: Discrete Hadley transform dwht: Discrete Walsh-Hadamard transform

Value

a Tensor-class object

If Eigenvalue decomposition is performed on a n x n x k tensor, the components in the returned value are:

P: A tensor of Eigenvectors (n x n x k)

D: An diagonal tensor of Eigenvalues (n x n x k)

Author(s)

Kyle Caudle

Randy Hoover

Jackson Cates

References

Kernfeld, E., Kilmer, M., & Aeron, S. (2015). Tensor-tensor products with invertible linear transforms. Linear Algebra and its Applications, 485, 545-570.

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 <- rand_tensor(modes=c(2,2,4))
tEIG(T,"dst")

rTensor2 documentation built on May 29, 2024, 8:34 a.m.