This package is centered around the `Tensor-class`

, which defines a S4 class for tensors of arbitrary number of modes. A vignette and/or a possible paper will be included in a future release of this package.

This page will summarize the full functionality of this package. Note that since all the methods associated with S4 class `Tensor-class`

are documented there, we will not duplicate it here.

The remaining functions can be split into two groups: the first is a set of tensor decompositions, and the second is a set of helper functions that are useful in tensor manipulation.

rTensor implements the following tensor decompositions:

`cp`

Canonical Polyadic (CP) decomposition

`tucker`

General Tucker decomposition

`mpca`

Multilinear Principal Component Analysis; note that for 3-Tensors this is also known as Generalized Low Rank Approximation of Matrices(GLRAM)

`hosvd`

(Truncated-)Higher-order singular value decomposition

`t_svd`

Tensor singular value decomposition; 3-Tensors only; also note that there is an asociated reconstruction function

`t_svd_reconstruct`

`pvd`

Population value decomposition of images; 3-Tensors only

rTensor also provides a set functions for tensors multiplication:

`ttm`

Tensor times matrix, aka m-mode product

`ttl`

Tensor times list (of matrices)

`t_mult`

Tensor product based on block circulant unfolding; only implemented for a pair of 3-Tensors

...as well as for matrices:

`hamadard_list`

Computes the Hamadard (element-wise) product of a list of matrices

`kronecker_list`

Computes the Kronecker product of a list of matrices

`khatri_rao`

Computes the Khatri-Rao product of two matrices

`khatri_rao_list`

Computes the Khatri-Rao product of a list of matrices

`fold`

General folding of a matrix into a tensor

`k_fold`

Inverse operation for

`k_unfold`

`unmatvec`

Inverse operation for

`matvec`

For more information on any of the functions, please consult the individual man pages.

James Li [email protected], Jacob Bien, and Martin T. Wells

jamesyili/rTensor documentation built on May 17, 2017, 9:44 a.m.

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