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The tensor can be flattened in three ways to obtain matrices comprising its mode-0, mode-1, and mode-2 vectors. [ 1 ] In multilinear algebra , mode-m flattening [ 1 ] [ 2 ] [ 3 ] , also known as matrixizing , matricizing , or unfolding , [ 4 ] is an operation that reshapes a multi-way array A {\displaystyle {\mathcal {A}}} into a matrix denoted ...
In other projects Wikidata item; ... (0,0) tensor is a number in the field ... For a tensor of order n, indices are raised by ...
The order of a tensor is the sum of these two numbers. The order (also degree or rank) of a tensor is thus the sum of the orders of its arguments plus the order of the resulting tensor. This is also the dimensionality of the array of numbers needed to represent the tensor with respect to a specific basis, or equivalently, the number of indices ...
the number of weighting functions are minimized per dimensions (hence the size of the core tensor); the weighting functions are one variable functions of the parameter vector in an orthonormed system for each parameter (singular functions); the sub tensors of the core tensor are also in orthogonal positions;
More generally, given two tensors (multidimensional arrays of numbers), their outer product is a tensor. The outer product of tensors is also referred to as their tensor product, and can be used to define the tensor algebra. The outer product contrasts with:
The number of each upper and lower indices of a tensor gives its type: a tensor with p upper and q lower indices is said to be of type (p, q), or to be a type-(p, q) tensor. The number of indices of a tensor, regardless of variance, is called the degree of the tensor (alternatively, its valence, order or rank, although rank is ambiguous).
A multi-way graph with K perspectives is a collection of K matrices ,..... with dimensions I × J (where I, J are the number of nodes). This collection of matrices is naturally represented as a tensor X of size I × J × K. In order to avoid overloading the term “dimension”, we call an I × J × K tensor a three “mode” tensor, where “modes” are the numbers of indices used to index ...
For a 3rd-order tensor , where is either or , Tucker Decomposition can be denoted as follows, = () where is the core tensor, a 3rd-order tensor that contains the 1-mode, 2-mode and 3-mode singular values of , which are defined as the Frobenius norm of the 1-mode, 2-mode and 3-mode slices of tensor respectively.