Skip to main content
Log in

Weighted Moore–Penrose inverses of arbitrary-order tensors

  • Published:
Computational and Applied Mathematics Aims and scope Submit manuscript

Abstract

Within the field of multilinear algebra, inverses and generalized inverses of tensors based on the Einstein product have been investigated over the past few years. The notion of the weighted Moore–Penrose inverses of even-order tensors in the framework of the Einstein product was introduced recently (Ji and Wei in Front Math China 12(6):1319–1337, 2017). In this article, we introduce the weighted Moore–Penrose inverse of an arbitrary-order tensor. We also investigate the singular value decomposition and full-rank decomposition of arbitrary-order tensors using reshape operation. Derived representations are used for two purposes: (1) to obtain a few new characterizations and representations of weighted Moore–Penrose inverse of arbitrary-order tensors; (2) to explore various necessary and sufficient conditions for the reverse-order law for the inverse to hold. In addition to these, we discuss applications of singular value decomposition and the Moore–Penrose inverse of an arbitrary-order tensor to a few 3D color image processing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Bader BW, Kolda TG (2006) Algorithm 862: MATLAB tensor classes for fast algorithm prototyping. ACM Trans Math Softw 32(4):635–653

    MathSciNet  MATH  Google Scholar 

  • Behera R, Mishra D (2017) Further results on generalized inverses of tensors via the einstein product. Linear Multilinear Algebra 65:1662–1682

    MathSciNet  MATH  Google Scholar 

  • Behera R, Nandi AK, Sahoo JK (2020) Further results on the drazin inverse of even-order tensors. Num Linear Algebra Appl e2317

  • Ben-Israel A, Greville T (1974) Generalized inverses: theory and application. Wiley, New York(NY)

    MATH  Google Scholar 

  • Beylkin G, Mohlenkamp M (2005) Algorithms for numerical analysis in high dimensions. SIAM J Sci Comput 26(6):2133–2159

    MathSciNet  MATH  Google Scholar 

  • Braman K (2010) Third-order tensors as linear operators on a space of matrices. Linear Algebra Appl 433(7):1241–1253

    MathSciNet  MATH  Google Scholar 

  • Brazell M, Li N, Navasca C, Tamon C (2013) Solving multilinear systems via tensor inversion. SIAM J Matrix Anal Appl 34(2):542–570

    MathSciNet  MATH  Google Scholar 

  • Che M, Bu C, Qi L, Wei Y (2018) Nonnegative tensors revisited: plane stochastic tensors. Linear Multilinear Algebra 1–28

  • Che M, Wei Y (2019) Randomized algorithms for the approximations of Tucker and the tensor train decompositions. Adv Comput Math 45(1):395–428

    MathSciNet  MATH  Google Scholar 

  • Che M, Wei Y (2020) Theory and computation of complex tensors and its applications. Springer, Berlin

    MATH  Google Scholar 

  • Chen Y, Qi L, Zhang X (2017) The fiedler vector of a laplacian tensor for hypergraph partitioning. SIAM J Sci Comput 39(6):A2508–A2537

    MathSciNet  MATH  Google Scholar 

  • Chew PA, Bader BW, Kolda TG, Abdelali A (2007) Cross-language information retrieval using parafac2. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 143–152

  • Coppi R, Bolasco S (1989) Multiway data analysis. Elsevier, Amsterdam

    MATH  Google Scholar 

  • de Silva V, Lim L (2008) Tensor rank and the ill-posedness of the best low-rank approximation problem. SIAM J Matrix Anal Appl 30(3):1084–1127

    MathSciNet  MATH  Google Scholar 

  • Ding W, Wei Y (2016) Solving multi-linear systems with \({\cal{M}}\)-tensors. J Sci Comput 68(2):689–715

    MathSciNet  MATH  Google Scholar 

  • Einstein A (2007) The foundation of the general theory of relativity. In: Kox AJ, Klein MJ, Schulmann R (eds) The Collected Papers of Albert Einstein 6. Princeton University Press, Princeton, NJ, pp 146–200

    Google Scholar 

  • Grasedyck L (2004) Existence and computation of low Kronecker-rank approximations for large linear systems of tensor product structure. Computing 72:247–265

    MathSciNet  MATH  Google Scholar 

  • Hitchcock FL (1927) The expression of a tensor or a polyadic as a sum of products. J Math Phys 6(1–4):164–189

    MATH  Google Scholar 

  • Huang B, Ma C (2020) Global least squares methods based on tensor form to solve a class of generalized Sylvester tensor equations. Appl Math Comput 369(124892):16

    MathSciNet  MATH  Google Scholar 

  • Huang B, Xie Y, Ma C (2019) Krylov subspace methods to solve a class of tensor equations via the Einstein product. Numer Linear Algebra Appl 26(4):e2254, 22

  • Ishteva M, Absil P-A, Van Huffel S, De Lathauwer L (2011) Best low multilinear rank approximation of higher-order tensors, based on the Riemannian trust-region scheme. SIAM J Matrix Anal Appl 32(1):115–135

    MathSciNet  MATH  Google Scholar 

  • Ji J, Wei Y (2017) Weighted Moore–Penrose inverses and fundamental theorem of even-order tensors with Einstein product. Front Math China 12(6):1319–1337

    MathSciNet  MATH  Google Scholar 

  • Ji J, Wei Y (2018) The Drazin inverse of an even-order tensor and its application to singular tensor equations. Comput Math Appl 75(9):3402–3413

    MathSciNet  MATH  Google Scholar 

  • Jin H, Bai M, BenÃtez J, Liu X (2017) The generalized inverses of tensors and an application to linear models. Comput Math Appl 74(3):385–397

    MathSciNet  Google Scholar 

  • Kilmer ME, Braman K, Hao N, Hoover RC (2013) Third-order tensors as operators on matrices: a theoretical and computational framework with applications in imaging. SIAM J Matrix Anal Appl 34(1):148–172

    MathSciNet  MATH  Google Scholar 

  • Kilmer ME, Martin CD (2011) Factorization strategies for third-order tensors. Linear Algebra Appl 435(3):641–658

    MathSciNet  MATH  Google Scholar 

  • Kolda TG (2001) Orthogonal tensor decompositions. SIAM J Matrix Anal Appl 23(1):243–255

    MathSciNet  MATH  Google Scholar 

  • Kolda TG, Bader BW (2009) Tensor decompositions and applications. SIAM Rev 51(3):455–500

    MathSciNet  MATH  Google Scholar 

  • Kruskal JB (1977) Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics. Linear Algebra Appl 18(2):95–138

    MathSciNet  MATH  Google Scholar 

  • Lai WM, Rubin D, Krempl E (2009) Introduction to continuum mechanics. Butterworth Heinemann, Oxford

    MATH  Google Scholar 

  • Lathauwer L, Moor B, Vandewalle J (2000) A multilinear singular value decomposition. SIAM J Matrix Anal Appl 21:1253–1278

    MathSciNet  MATH  Google Scholar 

  • Liang M, Zheng B (2019) Further results on Moore-Penrose inverses of tensors with application to tensor nearness problems. Comput Math Appl 77(5):1282–1293

    MathSciNet  MATH  Google Scholar 

  • Lyra-Leite DM, da Costa JPCL, de Carvalho JLA (2012) Improved MRI reconstruction and denoising using svd-based low-rank approximation. In: 2012 Workshop on Engineering Applications, pp 1–6. IEEE

  • Ma H, Li N, Stanimirović PS, Katsikis VN (2019) Perturbation theory for moore-penrose inverse of tensor via einstein product. Comput Appl Math 38(3):111

    MathSciNet  MATH  Google Scholar 

  • Martin CD, Shafer R, Larue B (2013) An order-\(p\) tensor factorization with applications in imaging. SIAM J Sci Comput 35(1):A474–A490

    MathSciNet  MATH  Google Scholar 

  • Martin CDM, Loan CFV (2008) A Jacobi-type method for computing orthogonal tensor decompositions. SIAM J Matrix Anal Appl 30(3):1219–1232

    MathSciNet  MATH  Google Scholar 

  • Panigrahy K, Behera R, Mishra D (2020) Reverse-order law for the Moore-Penrose inverses of tensors. Linear Multilinear Algebra 68(2):246–264

    MathSciNet  MATH  Google Scholar 

  • Panigrahy K, Mishra D (2020) Extension of moore–penrose inverse of tensor via einstein product. Linear Multilinear Algebra 1–24

  • Panigrahy K, Mishra D (2020) Reverse-order law for weighted Moore–Penrose inverse of tensors. Adv Oper Theory 5(1):39–63

    MathSciNet  MATH  Google Scholar 

  • Qi L (2005) Eigenvalues of a real supersymmetric tensor. J Symbol Comput 40:1302–1324

    MathSciNet  MATH  Google Scholar 

  • Ragnarsson S, Loan CFV (2012) Block tensor unfoldings. SIAM J Matrix Anal Appl 33(1):149–169

    MathSciNet  MATH  Google Scholar 

  • Sahoo JK, Behera R (2020) Reverse-order law for core inverse of tensors. Comput Appl Math 97(37):9

    MathSciNet  MATH  Google Scholar 

  • Sahoo JK, Behera R, Stanimirović PS, Katsikis VN (2020) Computation of outer inverses of tensors using the QR decomposition. Comput Appl Math 39(3):Paper No. 199, 20,

  • Sahoo JK, Behera RK, Stanimirović PS, Katsikis VN, Ma H (2020) Core and core-EP inverses of tensors. Comput Appl Math. 39(1):Paper No. 9

  • Shao J (2013) A general product of tensors with applications. Linear Algebra Appl 439:2350–2366

    MathSciNet  MATH  Google Scholar 

  • Shi X, Wei Y, Ling S (2013) Backward error and perturbation bounds for high order Sylvester tensor equation. Linear Multilinear Algebra 61:1436–1446

    MathSciNet  MATH  Google Scholar 

  • Shim Y, Cho Z (1981) SVD pseudo inversion image reconstruction. IEEE Trans Acoust Speech Signal Process 29(4):904–909

    Google Scholar 

  • Sidiropoulos ND, Lathauwer LD, Fu X, Huang K, Papalexakis EE, Faloutsos C (2017) Tensor decomposition for signal processing and machine learning. IEEE Trans Signal Process 65(13):3551–3582

    MathSciNet  MATH  Google Scholar 

  • Stanimirović PS, Ćirić M, Katsikis VN, Li C, Ma H (2020) Outer and (b, c) inverses of tensors. Linear Multilinear Algebra 68(5):940–971

    MathSciNet  Google Scholar 

  • Sun L, Zheng B, Bu C, Wei Y (2016) Moore–Penrose inverse of tensors via Einstein product. Linear Multilinear Algebra 64:686–698

    MathSciNet  MATH  Google Scholar 

  • Tian Y, Cheng S (2004) Some identities for moore-penrose inverses of matrix products. Linear Multilinear Algebra 52(6):405–420

    MathSciNet  MATH  Google Scholar 

  • Ye J (2005) Generalized low rank approximations of matrices. Mach Learn 61(1–3):167–191

    MATH  Google Scholar 

Download references

Acknowledgements

The authors would like to thanks the handling editor and referees for their detailed comments and suggestions. Ratikanta Behera is grateful for the supported by Science and Engineering Research Board (SERB), Department of Science and Technology, India, under the Grant No. EEQ/2017/000747. Ram N. Mohapatra is grateful to the Mohapatra Family Foundation and the College of Graduate Studies, University of Central Florida, Orlando, for their financial support for this research.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ratikanta Behera.

Additional information

Communicated by Ke Chen.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Behera, R., Maji, S. & Mohapatra, R.N. Weighted Moore–Penrose inverses of arbitrary-order tensors. Comp. Appl. Math. 39, 284 (2020). https://doi.org/10.1007/s40314-020-01328-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40314-020-01328-y

Keywords

Mathematics Subject Classification

Navigation