RESEARCH ARTICLE

Tensor Bernstein concentration inequalities with an application to sample estimators for high-order moments

  • Ziyan LUO 1 ,
  • Liqun QI , 2,3 ,
  • Philippe L. TOINT 4
Expand
  • 1. Department of Applied Mathematics, School of Science, Beijing Jiaotong University, Beijing 100044, China
  • 2. Department of Mathematics, School of Science, Hangzhou Dianzi University, Hangzhou 310018, China
  • 3. Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
  • 4. Namur Center for Complex Systems (naXys), University of Namur, 61, rue de Bruxelles, B-5000 Namur, Belgium

Received date: 16 Mar 2020

Accepted date: 03 Apr 2020

Published date: 15 Apr 2020

Copyright

2020 Higher Education Press

Abstract

This paper develops the Bernstein tensor concentration inequality for random tensors of general order, based on the use of Einstein products for tensors. This establishes a strong link between these and matrices, which in turn allows exploitation of existing results for the latter. An interesting application to sample estimators of high-order moments is presented as an illustration.

Cite this article

Ziyan LUO , Liqun QI , Philippe L. TOINT . Tensor Bernstein concentration inequalities with an application to sample estimators for high-order moments[J]. Frontiers of Mathematics in China, 2020 , 15(2) : 367 -384 . DOI: 10.1007/s11464-020-0830-4

1
Aclioptas D, McSherry F. Fast computation of low rank matrix approximations. In: Proc 33rd Ann ACM Symposium on Theory of Computing. 2001, 611–618

DOI

2
Ahlswede R, Winter A. Strong converse for identification via quantum channels. IEEE Trans Inform Theory, 2002, 48(3): 569–579

DOI

3
Bellavia S, Gurioli G, Morini B. Theoretical study of an adaptive cubic regularization method with dynamic inexact Hessian information. arXiv: 1808.06239

4
Bellavia S, Gurioli G, Morini B, Toint Ph L. Deterministic and stochastic inexact regularization algorithms for nonconvex optimization with optimal complexity. arXiv: 1811.03831

5
Bellavia S, Krejić N, Krklec Jerinkić N. Subsampled inexact Newton methods for minimizing large sums of convex function. arXiv: 1811.05730

6
Brazell M, Li N, Navasca C, Tamon Ch. Solving multilinear systems via tensor inversion. SIAM J Matrix Anal Appl, 2013, 34(2): 542–570

DOI

7
Cartwright D, Sturmfels B. The number of eigenvalues of a tensor. Linear Algebra Appl, 2013, 438(2): 942–952

DOI

8
Chen X, Jiang B, Lin T, Zhang S. On adaptive cubic regularization Newton's methods for convex optimization via random sampling. arXiv: 1802.05426

9
Donoho D L. Compressed sensing. IEEE Trans Inform Theory, 2006, 52(4): 1289–1306

DOI

10
Einstein A. The foundation of the general theory of relativity. In: The Collected Papers of Albert Einstein, Vol 6. Princeton: Princeton Univ Press, 1997, 146–200

11
Forrester P J. Log-Gases and Random Matrices. London Math Soc Monogr Ser, 34. Princeton: Princeton Univ Press, 2010

DOI

12
Frieze A, Kannan R, Vempala S. Fast Monte-Carlo algorithms for finding low-rank approximations. In: Proc 39th Ann Symposium on Foundations of Computer Science. 1998, 370–378

13
Kohler J M. Lucchi A. Sub-sample cubic regularization for non-convex optimization. In: Proc 34th International Conference on Machine Learning, Sydney, 2017. arXiv: 1705.05933v3

14
Lai W M, Rubin D H, Krempl E. Introduction to Continuum Mechanics. Oxford: Butterworth-Heinemann, 2009

DOI

15
Luo Z, Qi L, Ye Y. Linear operators and positive semidefiniteness of symmetric tensor spaces. Sci China Math, 2015, 58(1): 197–212

DOI

16
Miao Y, Qi L, Wei Y. Generalized tensor function via the tensor singular value decomposition based on the T-product. Linear Algebra Appl, 2020, 590: 258–303

DOI

17
Qi L. Eigenvalues of a real supersymmetric tensor. J Symbolic Comput, 2005, 40(6): 1302–1324

DOI

18
Qi L, Luo Z. Tensor Analysis: Spectral Theory and Special Tensors. Philadelphia: SIAM, 2017

DOI

19
Qi L, Yu G, Wu E X. Higher order positive semidefinite diffusion tensor imaging. SIAM J Imaging Sci, 2010, 3(3): 416–433

DOI

20
Sun L, Zheng B, Bu C, Wei Y. Moore-Penrose inverse of tensors via Einstein product. Linear Multilinear Algebra, 2016, 64(4): 686–698

DOI

21
Thomson W. Elements of a mathematical theory of elasticity. Proc Roy Soc Lond, 1856, 8: 85–87

DOI

22
Tropp J. An Introduction to Matrix Concentration Inequalities. Found Trends Machine Learning, Number 8: 1-2. Boston: Now Publ, 2015

DOI

23
Williams C K I, Seeger N. Using the Nystrom method to speed up kernel machines. In: Advances in Neural Information Processing Systems 13. 2001, 682–688

24
Wishart J. The generalized product moment distribution in samples from a multivariate normal population. Biometrika, 1928, 20A(1-2): 32–52

DOI

25
Xu P, Roosta-Khorasani F, Mahoney M W. Newton-type methods for non-convex optimization under inexact Hessian information. arXiv: 1708.07164v3

26
Xu P, Roosta-Khorasani F, Mahoney M W. Second-order optimization for non-convex machine learning: an empirical study. arXiv: 1708.07827v2

Outlines

/