Multidimensional Arrays ("Tensors") can be unfolded, i.e. multiple dimensions can be combined into a single dimension in a block-wise fashion. Such unfoldings are central to tensor decomposition. In general, computations on tensors are regularly performed by reducing tensors to matrices ("2-dimensional tensors") and then use regular matrix algebra.

1 2 3 4 | ```
unfold(x, MARGINS)
unfold_to_matrix(x, ROWS, COLS = NULL)
tenmat(x, ROWS, COLS = NULL)
``` |

`x` |
Sparse array to be unfolded, using |

`MARGINS` |
Margins ("dimensions") to be unfolded. The margins specified will be turned into a single dimension, to be added as the last dimension of the resulting array (see Details). |

`ROWS` |
Margins of the original array to be unfolded into the rows of the resulting matrix. |

`COLS` |
Margins of the original array to be unfolded into the columns of the resulting matrix. If |

The function `unfold`

is a general approach to combining of multiple dimensions into a single dimensions. The function `unfold_to_matrix`

is a special case in which the result is a 2-dimensional matrix. This second function is made to emulate the functionality of the `tenmat`

("tensor to matrix") from the Matlab Tensor Toolbox. For convenience, the function-name `tenmat`

is also added as a synonym for `unfold_to_matrix`

.

Unfolding basically works by interspercing margins subsequently. E.g. margin A of size 3 (A1, A2, A3) and a margin B of size 2 (B1, B2) are unfolded through `c(A,B)`

as (A1B1, A2B1, A3B1, A1B2, A2B2, A3B2), but they are unfolded through `c{B,A}`

as (B1A1, B2A1, B1A2, B2A2, B1A3, B2A3).

`unfold`

returns a `simple_sparse_array`

with the new combined dimension added as the last dimension. All original dimensions are shifted forward. The relation between the original dimensions and the new dimensions is stored as an `permutation`

attribute, e.g. try `attr(x, "p")`

. When multiple unfoldings are performed after each other, these permutations can be subsetted on each other to obtain the final permutation. See examples below.

`unfold_to_matrix`

and `tenmat`

return a sparse matrix of class `dgTMatrix`

.

Michael Cysouw <cysouw@mac.com>

see http://www.cs.cornell.edu/cv/SummerSchool/Unfold.pdf for some notes on tensor unfolding. The Matlab Tensor Toolbox can be found at http://www.sandia.gov/~tgkolda/TensorToolbox/index-2.6.html. A newer Matlab implementation is http://www.tensorlab.net.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | ```
# example from \url{http://www.cs.cornell.edu/cv/SummerSchool/Unfold.pdf}
x <- array(c(111, 211, 311, 411, 121, 221, 321,
421, 131, 231, 331, 431, 112, 212, 312, 412,
122, 222, 322, 422, 132, 232, 332, 432), dim = c(4, 3, 2))
x
s <- as.simple_sparse_array(x)
( s1 <- as.array(unfold_to_matrix(s,1)) )
# note this is identical to:
( s23 <- as.array(unfold(s,c(2,3))) )
all.equal(s23, s1)
# larger example from same source
x <- array(0, dim = c(2,3,2,2,3))
x[1,2,1,1,2] <- 12112
x[2,3,1,2,2] <- 23122
x[2,2,2,1,1] <- 22211
x[2,2,1,2,3] <- 22123
s <- as.simple_sparse_array(x)
as.array(unfold_to_matrix(s, c(1,2,3), c(4,5)))
# use attribute "permutation" to track dimensions
# first step: unfold 1,2,3 to become dimension 3
# original dimensions 4,5 now become 1,2
s1 <- unfold(s, c(1,2,3))
( p1 <- attr(s1, "permutation") )
# now take these dimension 1,2 (originally 4,5) and unfold them
s2 <- unfold(s1, c(1,2))
( p2 <- attr(s2, "permutation") )
# use subsetting to track dimensions through subsequent unfolding
p2[p1]
``` |

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