A set of tools for creation, manipulation, and modeling
of tensors with arbitrary number of modes. A tensor in the context of data
analysis is a multidimensional array. rTensor does this by providing a S4
class 'Tensor' that wraps around the base 'array' class. rTensor
provides common tensor operations as methods, including matrix unfolding,
summing/averaging across modes, calculating the Frobenius norm, and taking
the inner product between two tensors. Familiar array operations are
overloaded, such as index subsetting via '[' and element-wise operations.
rTensor also implements various tensor decomposition, including CP, GLRAM,
MPCA, PVD, and Tucker. For tensors with 3 modes, rTensor also implements
transpose, t-product, and t-SVD, as defined in Kilmer et al. (2013). Some
auxiliary functions include the Khatri-Rao product, Kronecker product, and
the Hadamard product for a list of matrices.

Tools for Tensor Analysis and Decomposition

package-ubuntu-20.04-r-4.0.5#cran#rTensor-1.4.8

<p>Reconstruct the original 3-Tensor after it has been decomposed into <code>U, S, V</code> via <code><a href="t_svd.html">t_svd</a></code>.
</p>

<table summary="R argblock"><tr valign="top">
<td><code>L</code></td>
<td>
<p>list that is an output from <code><a href="t_svd.html">t_svd</a></code></p>
</td>
</tr></table>

tnsr <- rand_tensor(c(10,10,10))
tsvdD <- t_svd(tnsr)
1 - fnorm(t_svd_reconstruct(tsvdD)-tnsr)/fnorm(tnsr)

package-ubuntu-20.04-r-4.0.5#cran#rTensor-1.4.8#docs#t_svd_reconstruct

t_svd_reconstruct

Description

Usage

Arguments

Value

See Also

Examples

rTensor

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