hosvd-methods | R Documentation |

Higher-order SVD of a K-Tensor.
Write the K-Tensor as a (m-mode) product of a core Tensor
(possibly smaller modes) and K orthogonal factor matrices.
Truncations can be specified via `ranks`

(making them smaller than the original modes of the K-Tensor will
result in a truncation).
For the mathematical details on HOSVD, consult Lathauwer et. al. (2000).

hosvd(darr, ranks=NULL) ## S4 method for signature 'DelayedArray' hosvd(darr, ranks)

`darr` |
Tensor with K modes |

`ranks` |
a vector of desired modes in the output core tensor,
default is |

This function is an extension of the `hosvd`

by DelayedArray.

A progress bar is included to help monitor operations on large tensors.

a list containing the following:

`Z`

core tensor with modes speficied by

`ranks`

`U`

a list of orthogonal matrices, one for each mode

`est`

estimate of

`darr`

after compression`fnorm_resid`

the Frobenius norm of the error

`fnorm(est-darr)`

- if there was no truncation, then this is on the order of mach_eps * fnorm.

The length of `ranks`

must match `darr@num_modes`

.

L. Lathauwer, B.Moor, J. Vanderwalle "A multilinear singular value decomposition". Journal of Matrix Analysis and Applications 2000.

`tucker`

library("DelayedRandomArray") darr <- RandomUnifArray(c(3,4,5)) hosvd(darr, ranks=c(2,1,3))

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.