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A study on T-eigenvalues of third-order tensors. (English) Zbl 1458.15020

Summary: In this paper, we study T-eigenvalues of third-order tensors. Definitions of the T-eigenvalues and Hermitian tensors are proposed. We present a commutative tensor family. We prove some T-eigenvalue inequalities for Hermitian tensors, including extensions of Weyl’s theorem and Cauchy’s interlacing theorem from the matrix case to the tensor case. Finally, we introduce the stability of the T-eigenvalues and study the Lyapunov equation for tensors.

MSC:

15A18 Eigenvalues, singular values, and eigenvectors
15A69 Multilinear algebra, tensor calculus
Full Text: DOI

References:

[1] Braman, K., Third-order tensors as linear operators on a space of matrices, Linear Algebra Appl., 433, 1241-1253 (2010) · Zbl 1198.15017
[2] Chan, T.; Yang, T., Polar n-complex and n-bicomplex singular value decomposition and principal component pursuit, IEEE Trans. Signal Process., 64, 6533-6544 (2016) · Zbl 1414.94108
[3] Gleich, D.; Greif, C.; Varah, J., The power and Arnoldi methods in an algebra of circulants, Numer. Linear Algebra Appl., 20, 809-831 (2013) · Zbl 1313.65079
[4] Hao, N.; Kilmer, M.; Braman, K.; Hoover, R., Facial recognition using tensor-tensor decompositions, SIAM J. Imaging Sci., 6, 437-463 (2013) · Zbl 1305.15061
[5] Horn, R.; Johnson, C., Matrix Analysis (2013), Cambridge University Press: Cambridge University Press Cambridge · Zbl 1267.15001
[6] Horn, R.; Johnson, C., Topics in Matrix Analysis (1994), Cambridge University Press: Cambridge University Press Cambridge · Zbl 0801.15001
[7] Hu, W.; Yang, Y.; Zhang, W.; Xie, Y., Moving object detection using tensor-based low-rank and saliently fused-sparse decomposition, IEEE Trans. Image Process., 26, 724-737 (2017) · Zbl 1409.94251
[8] Jin, X.; Vong, S., An Introduction to Applied Matrix Analysis (2016), Higher Education Press: Higher Education Press Beijing · Zbl 1343.65027
[9] Kilmer, M.; Braman, K.; Hao, N.; Hoover, R., Third-order tensors as operators on matrices: a theoretical and computational framework with applications in imaging, SIAM J. Matrix Anal. Appl., 34, 148-172 (2013) · Zbl 1269.65044
[10] Kilmer, M.; Martin, C., Factorization strategies for third-order tensors, Linear Algebra Appl., 435, 641-658 (2011) · Zbl 1228.15009
[11] Kong, H.; Xie, X.; Lin, Z., t-Schatten-p norm for low-rank tensor recovery, IEEE J. Sel. Top. Signal Process., 12, 1405-1419 (2018)
[12] Liu, T.; Chen, L.; Zhu, C., Improved robust tensor principal component analysis via low-rank core matrix, IEEE J. Sel. Top. Signal Process., 12, 1378-1389 (2018)
[13] Long, Z.; Liu, Y.; Chen, L.; Zhu, C., Low rank tensor completion for multiway visual data, Signal Process., 155, 301-316 (2019)
[14] Lund, K., The tensor t-function: a definition for functions of third-order tensors, Numer. Linear Algebra Appl. (2020) · Zbl 1463.15052
[15] Martin, C.; Shafer, R.; Larue, B., An order-p tensor factorization with applications in imaging, SIAM J. Sci. Comput., 35, A474-A490 (2013) · Zbl 1273.15032
[16] Miao, Y.; Qi, L.; Wei, Y., T-Jordan canonical form and T-Drazin inverse based on the T-product, Commun. Appl. Math. Comput. (2020)
[17] Newman, E.; Horesh, L.; Avron, H.; Kilmer, M., Stable tensor neural networks for rapid deep learning (2018), arXiv preprint
[18] Soltani, S.; Kilmer, M.; Hansen, P., A tensor-based dictionary learning approach to tomographic image reconstruction, BIT Numer. Math., 56, 1425-1454 (2016) · Zbl 1355.65036
[19] Stewart, G.; Sun, J., Matrix Perturbation Theory (1990), Academic Press: Academic Press Boston · Zbl 0706.65013
[20] Tarzanagh, D.; Michailidis, G., Fast randomized algorithms for t-product based tensor operations and decompositions with applications to imaging data, SIAM J. Imaging Sci., 11, 2629-2664 (2018) · Zbl 07115010
[21] Wang, A.; Lai, Z.; Jin, Z., Noisy low-tubal-rank tensor completion, Neurocomputing, 330, 267-279 (2019)
[22] Zheng, M.; Huang, Z.; Wang, Y., T-positive semidefiniteness of third-order symmetric tensors and T-semidefinite programming, Comput. Optim. Appl. (2020)
[23] Zoltowski, D.; Dhingra, N.; Lin, F.; Jovanović, M., Sparsity-promoting optimal control of spatially-invariant systems, (2014 American Control Conference. 2014 American Control Conference, Portland, OR (2014)), 1255-1260
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