Tensor decomposition

In multilinear algebra, a tensor decomposition is any scheme for expressing a "data tensor" (M-way array) as a sequence of elementary operations acting on other, often simpler tensors. Many tensor decompositions generalize some matrix decompositions.

Tensors are generalizations of matrices to higher dimensions (or rather to higher orders, i.e. the higher number of dimensions) and can consequently be treated as multidimensional fields. The main tensor decompositions are:

• Tensor rank decomposition;
• Higher-order singular value decomposition;
• Tucker decomposition;
• matrix product states, and operators or tensor trains;
• Online Tensor Decompositions
• hierarchical Tucker decomposition;
• block term decomposition

Notation

This section introduces basic notations and operations that are widely used in the field.

Introduction

A multi-way graph with K perspectives is a collection of K matrices ${\displaystyle {X_{1},X_{2}.....X_{K}}}$ 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 the tensor.

References

1. Tensor rank decomposition
2. Tensor (machine learning)
3. Higher-order singular value decomposition
4. Tensor calculus
5. Scalar–vector–tensor decomposition
6. Tucker decomposition
7. Schmidt decomposition
8. Tensor
9. Singular value decomposition
10. Tensor software
11. Elasticity tensor
12. Helmholtz decomposition
13. Knowledge graph embedding
14. Weyl tensor
15. Kronecker product
16. Antisymmetric tensor
17. Ricci decomposition
18. Symmetric tensor
19. Generalized singular value decomposition
20. Multidimensional network