Mode-k flattening

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Short description: Mathematical operation
Flattening a (3rd-order) tensor. 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 into a matrix denoted by A[m] (a two-way array).

Matrixizing may be regarded as a generalization of the mathematical concept of vectorizing.

Definition

The mode-m matrixizing of tensor 𝒜I0×I1××IM, is defined as the matrix 𝐀[m]Im×(I0Im1Im+1IM). As the parenthetical ordering indicates, the mode-m column vectors are arranged by sweeping all the other mode indices through their ranges, with smaller mode indexes varying more rapidly than larger ones; thus[1]

[𝐀[m]]jk=ai1imiM, where j=im and k=1+n=0nmM(in1)=0mn1I. By comparison, the matrix 𝐀[m]Im×(Im+1IMI0I1Im1) that results from an unfolding[4] has columns that are the result of sweeping through all the modes in a circular manner beginning with mode m + 1 as seen in the parenthetical ordering. This is an inefficient way to matrixize.[citation needed]

Applications

This operation is used in tensor algebra and its methods, such as Parafac and HOSVD.[citation needed]

References

  1. ↑ 1.0 1.1 1.2 Vasilescu, M. Alex O. (2009), "Multilinear (Tensor) Algebraic Framework for Computer Graphics, Computer Vision and Machine Learning", University of Toronto: p. 21, https://tspace.library.utoronto.ca/bitstream/1807/65327/11/Vasilescu_M_Alex_O_200911_PhD_thesis.pdf 
  2. ↑ Vasilescu, M. Alex O.; Terzopoulos, Demetri (2002), "Multilinear Analysis of Image Ensembles: TensorFaces", Computer Vision — ECCV 2002 (Berlin, Heidelberg: Springer Berlin Heidelberg): pp. 447–460, doi:10.1007/3-540-47969-4_30, ISBN 978-3-540-43745-1, http://dx.doi.org/10.1007/3-540-47969-4_30, retrieved 2023-03-15 
  3. ↑ EldĂ©n, L.; Savas, B. (2009-01-01), "A Newton–Grassmann Method for Computing the Best Multilinear Rank-(r1,r2,r3) Approximation of a Tensor", SIAM Journal on Matrix Analysis and Applications 31 (2): 248–271, doi:10.1137/070688316, ISSN 0895-4798, http://epubs.siam.org/doi/10.1137/070688316 
  4. ↑ 4.0 4.1 De Lathauwer, Lieven; De Mood, B.; Vandewalle, J. (2000), "A multilinear singular value decomposition", SIAM Journal on Matrix Analysis and Applications 21 (4): 1253–1278, doi:10.1137/S0895479896305696, http://www.siam.org/journals/simax/21-4/30569.html