Skip to main content
Log in

Maximizing sum of coupled traces with applications

  • Published:
Numerische Mathematik Aims and scope Submit manuscript

Abstract

This paper concerns maximizing the sum of coupled traces of quadratic and linear matrix forms. The coupling comes from requiring the matrix variables in the quadratic and linear matrix forms to be packed together to have orthonormal columns. At a maximum, the KKT condition becomes a nonlinear polar decomposition (NPD) of a matrix-valued function with dependency on the orthogonal polar factor. A self-consistent-field iteration, along with a locally optimal conjugate gradient (LOCG) acceleration, are proposed to compute the NPD. It is proved that both methods are convergent and it is demonstrated numerically that the LOCG acceleration is very effective. As applications, we demonstrate our methods on the MAXBET subproblem and the multi-view partially shared subspace learning (MvPS) subproblem, both of which sit at the computational kernels of two multi-view subspace learning models. In particular, we also demonstrate MvPS on several real world data sets.

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

Notes

  1. Computing the SVD of an \(n\times k\) matrix takes \(6nk^2+20k^3\) flops [18, p.493].

  2. A MATLAB toolbox for OPTimization on MANifolds available online at https://www.manopt.org/.

  3. A toolbox for STiefel manifold OPtimization available online at https://stmopt.gitee.io/.

  4. All numerical demonstrations in this paper were done in MATLAB on a laptop with Intel(R) Core(TM) i7-1165G7 CPU 2.80 GHz and 32GB memory, except those in Sect. 5.2.2 which were performed on an EXXACT workstation (www.exxactcorp.com).

  5. Up to this point, \(X_j\) as in (1.1) has been used for an orthonormal projection matrix. For the rest of this section, we will use them as data matrices as is done conventionally. Hopefully, no confusion will arise.

References

  1. Absil, P.A., Mahony, R., Sepulchre, R.: Optimization Algorithms on Matrix Manifolds. Princeton University Press, Princeton (2008)

    Book  MATH  Google Scholar 

  2. Anderson, E., Bai, Z., Bischof, C., Demmel, J., Dongarra, J., Croz, J.D., Greenbaum, A., Hammarling, S., McKenney, A., Ostrouchov, S., Sorensen, D.: LAPACK Users’ Guide, 3rd edn. SIAM, Philadelphia (1999)

    Book  MATH  Google Scholar 

  3. Bai, Z., Demmel, J., Dongarra, J., Ruhe, A., van der Vorst, H. (eds.): Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide. SIAM, Philadelphia (2000)

    MATH  Google Scholar 

  4. Bai, Z., Li, R.C., Lu, D.: Sharp estimation of convergence rate for self-consistent field iteration to solve eigenvector-dependent nonlinear eigenvalue problems. SIAM J. Matrix Anal. Appl. 43(1), 301–327 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  5. Balogh, J., Csendes, T., Rapcsá, T.: Some global optimization problems on Stiefel manifolds. J. Glob. Optim. 30, 91–101 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  6. Birtea, P., Caşu, I., Comănescu, D.: First order optimality conditions and steepest descent algorithm on orthogonal Stiefel manifolds. Opt. Lett. 13, 1773–1791 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  7. Bolla, M., Michaletzky, G., Tusnády, G., Ziermann, M.: Extrema of sums of heterogeneous quadratic forms. Linear Algebra Appl. 269(1), 331–365 (1998). https://doi.org/10.1016/S0024-3795(97)00230-9

    Article  MathSciNet  MATH  Google Scholar 

  8. Borg, I., Lingoes, J.: Multidimensional Similarity Structure Analysis. Springer-Verlag, New York (1987)

    Book  MATH  Google Scholar 

  9. Boumal, N., Mishra, B., Absil, P.A., Sepulchre, R.: Manopt, a Matlab toolbox for optimization on manifolds. J. Mach. Learn. Res. 15(42), 1455–1459 (2014)

    MATH  Google Scholar 

  10. Cai, Y., Zhang, L.H., Bai, Z., Li, R.C.: On an eigenvector-dependent nonlinear eigenvalue problem. SIAM J. Matrix Anal. Appl. 39(3), 1360–1382 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  11. Chu, M.T., Trendafilov, N.T.: The orthogonally constrained regression revisited. J. Comput. Graph. Stat. 10(4), 746–771 (2001)

    Article  MathSciNet  Google Scholar 

  12. Cunningham, J.P., Ghahramani, Z.: Linear dimensionality reduction: survey, insights, and generalizations. J. Mach. Learn. Res. 16, 2859–2900 (2015)

    MathSciNet  MATH  Google Scholar 

  13. Demmel, J.: Applied Numerical Linear Algebra. SIAM, Philadelphia (1997)

    Book  MATH  Google Scholar 

  14. Edelman, A., Arias, T.A., Smith, S.T.: The geometry of algorithms with orthogonality constraints. SIAM J. Matrix Anal. Appl. 20(2), 303–353 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  15. Eldén, L., Park, H.: A procrustes problem on the Stiefel manifold. Numer. Math. 82, 599–619 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  16. Fan, K.: On a theorem of Weyl concerning eigenvalues of linear transformations. I. Proc. Natl. Acad. Sci. USA 35(11), 652–655 (1949)

    Article  MathSciNet  Google Scholar 

  17. Gao, B., Liu, X., Chen, X., Yuan, Y.X.: A new first-order algorithmic framework for optimization problems with orthogonality constraints. SIAM J. Optim. 28(1), 302–332 (2018). https://doi.org/10.1137/16M1098759

    Article  MathSciNet  MATH  Google Scholar 

  18. Golub, G.H., Van Loan, C.F.: Matrix Computations, 4th edn. Johns Hopkins University Press, Baltimore (2013)

    MATH  Google Scholar 

  19. Gower, J.C., Dijksterhuis, G.B.: Procrustes Problems. Oxford University Press, New York (2004)

    Book  MATH  Google Scholar 

  20. Hardoon, D.R., Szedmak, S., Shawe-Taylor, J.: Canonical correlation analysis: an overview with application to learning methods. Neural Comput. 16, 2639–2664 (2004)

    Article  MATH  Google Scholar 

  21. Horn, R.A., Johnson, C.R.: Matrix Analysis, 2nd edn. Cambridge University Press, New York (2013)

    MATH  Google Scholar 

  22. Hotelling, H.: Relations between two sets of variates. Biometrika 28(3–4), 321–377 (1936)

    Article  MATH  Google Scholar 

  23. Hurley, J.R., Cattell, R.B.: The Procrustes program: producing direct rotation to test a hypothesized factor structure. Comput. Behav. Sci. 7, 258–262 (1962)

    Article  Google Scholar 

  24. Imakura, A., Li, R.C., Zhang, S.L.: Locally optimal and heavy ball GMRES methods. Jpn. J. Ind. Appl. Math. 33, 471–499 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  25. Kanzow, C., Qi, H.D.: A QP-free constrained Newton-type method for variational inequality problems. Math. Program. 85, 81–106 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  26. Knyazev, A.V.: Toward the optimal preconditioned eigensolver: locally optimal block preconditioned conjugate gradient method. SIAM J. Sci. Comput. 23(2), 517–541 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  27. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 2169–2178. IEEE (2006)

  28. Li, F.F., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. Comput. Vis. Image Underst. 106(1), 59–70 (2007)

    Article  Google Scholar 

  29. Li, L., Zhang, Z.: Semi-supervised domain adaptation by covariance matching. IEEE Trans. Pattern Anal. Mach. Intell. 41(11), 2724–2739 (2019). https://doi.org/10.1109/TPAMI.2018.2866846

    Article  Google Scholar 

  30. Li, R.C.: New perturbation bounds for the unitary polar factor. SIAM J. Matrix Anal. Appl. 16, 327–332 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  31. Li, R.C.: Relative perturbation bounds for the unitary polar factor. BIT 37, 67–75 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  32. Li, R.C.: Matrix perturbation theory. In: Hogben, L., Brualdi, R., Stewart, G.W. (eds.) Handbook of Linear Algebra, 2nd edn, Chapter 21. CRC Press, Boca Raton (2014)

  33. Li, R.C.: Rayleigh quotient based optimization methods for eigenvalue problems. In: Bai, Z., Gao, W., Su, Y. (eds.) Matrix Functions and Matrix Equations, Series in Contemporary Applied Mathematics, vol. 19, pp. 76–108. World Scientific, Singapore (2015)

    Chapter  Google Scholar 

  34. Li, Y., Yang, M., Zhang, Z.: A survey of multi-view representation learning. IEEE Trans. Knowl. Data Eng. 31(10), 1863–1883 (2018)

    Article  Google Scholar 

  35. Liu, X.G., Wang, X.F., Wang, W.G.: Maximization of matrix trace function of product Stiefel manifolds. SIAM J. Matrix Anal. Appl. 36(4), 1489–1506 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  36. Ma, X., Shen, C., Wang, L., Zhang, L.H., Li, R.C.: A self-consistent-field iteration for MAXBET with an application to multi-view feature extraction. Adv. Comput. Math. 48, 13 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  37. Ma, X., Wang, L., Zhang, L.H., Shen, C., Li, R.C.: Multi-view partially shared subspace learning (2021). Submitted

  38. Moré, J., Sorensen, D.: Computing a trust region step. SIAM J. Sci. Stat. Comput. 4(3), 553–572 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  39. Nie, F., Zhang, R., Li, X.: A generalized power iteration method for solving quadratic problem on the Stiefel manifold. Sci. China Inf. Sci. 60, 1–10 (2017)

    Article  MathSciNet  Google Scholar 

  40. Nielsen, A.A.: Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data. IEEE Trans. Image Process. 11(3), 293–305 (2002)

    Article  Google Scholar 

  41. Nocedal, J., Wright, S.: Numerical Optimization, 2nd edn. Springer (2006)

  42. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  MATH  Google Scholar 

  43. Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)

    Article  MATH  Google Scholar 

  44. Parlett, B.N.: The Symmetric Eigenvalue Problem. SIAM, Philadelphia,: This SIAM edition is an unabridged, corrected reproduction of the work first published by Prentice-Hall Inc, p. 1980. Englewood Cliffs, New Jersey (1998)

  45. Polyak, B.T.: Introduction to Optimization. Optimization Software, New York (1987)

    MATH  Google Scholar 

  46. Rapcsák, T.: On minimization on Stiefel manifolds. Eur. J. Oper. Res. 143(2), 365–376 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  47. Saad, Y.: Numerical Methods for Large Eigenvalue Problems. Manchester University Press, Manchester (1992)

    MATH  Google Scholar 

  48. Sharma, A., Kumar, A., Daume, H., Jacobs, D.W.: Generalized multiview analysis: a discriminative latent space. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2160–2167 (2012)

  49. Stewart, G.W.: Matrix Algorithms, Eigensystems, vol. II. SIAM, Philadelphia (2001)

    Book  MATH  Google Scholar 

  50. Sun, J.G.: Matrix Perturbation Analysis. Graduate Texts (Academia, Sinica), 2nd edn. Science Publisher, Beijing (2001). (in Chinese)

  51. Takahashi, I.: A note on the conjugate gradient method. Inf. Process. Jpn. 5, 45–49 (1965)

    MathSciNet  MATH  Google Scholar 

  52. Ten Berge, J.M.F.: Generalized approaches to the MAXBET problem and the MAXDIFF problem, with applications to canonical correlations. Psychometrika 53(4), 487–494 (1984)

    Article  MATH  Google Scholar 

  53. Van de Geer, J.P.: Linear relations among \(k\) sets of variables. Psychometrika 49(1), 70–94 (1984)

    Google Scholar 

  54. Wang, L., Gao, B., Liu, X.: Multipliers correction methods for optimization problems over the Stiefel manifold. CSIAM Trans. Appl. Math. 2(3), 508–531 (2021). https://doi.org/10.4208/csiam-am.SO-2020-0008

    Article  MathSciNet  Google Scholar 

  55. Wang, L., Li, R.C.: A scalable algorithm for large-scale unsupervised multi-view partial least squares. IEEE Trans. Big Data (2020). https://doi.org/10.1109/TBDATA.2020.3014937

    Article  Google Scholar 

  56. Wen, Z., Yin, W.: A feasible method for optimization with orthogonality constraints. Math. Program. 142(1–2), 397–434 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  57. Wu, J., Rehg, J.M.: Where am I: place instance and category recognition using spatial pact. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008)

  58. Yang, M., Li, R.C.: Heavy ball flexible GMRES method for nonsymmetric linear systems. J. Comput. Math. (2021). To appear

  59. Zhang, L.H.: Riemannian trust-region method for the maximal correlation problem. Numer. Funct. Anal. Optim. 33(3), 338–362 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  60. Zhang, L.H., Li, R.C.: Maximization of the sum of the trace ratio on the Stiefel manifold, I: theory. Sci. China Math. 57(12), 2495–2508 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  61. Zhang, L.H., Li, R.C.: Maximization of the sum of the trace ratio on the Stiefel manifold, II: computation. Sci. China Math. 58(7), 1549–1566 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  62. Zhang, L.H., Wang, L., Bai, Z., Li, R.C.: A self-consistent-field iteration for orthogonal canonical correlation analysis. IEEE Trans. Pattern Anal. Mach. Intell. 44(2), 890–904 (2022). https://doi.org/10.1109/TPAMI.2020.3012541

    Article  Google Scholar 

  63. Zhang, L.H., Yang, W.H., Shen, C., Ying, J.: An eigenvalue-based method for the unbalanced Procrustes problem. SIAM J. Matrix Anal. Appl. 41(3), 957–983 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  64. Zhang, Z., Du, K.: Successive projection method for solving the unbalanced procrustes problem. Sci. China Math. 49(7), 971–986 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  65. Zhao, H., Wang, Z., Nie, F.: Orthogonal least squares regression for feature extraction. Neurocomputing 216, 200–207 (2016)

    Article  Google Scholar 

  66. Zhou, Y., Li, R.C.: Bounding the spectrum of large Hermitian matrices. Linear Algebra Appl. 435, 480–493 (2011)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The authors wish to thank the two anonymous referees for their constructive suggestions that greatly improved the presentation of this paper. They are indebted to Prof. M. Overton of New York University for his numerous minor but important corrections across the manuscript. Wang was supported in part by NSF DMS-2009689; Zhang was supported in part by the National Natural Science Foundation of China NSFC-12071332; Li was supported in part by NSF DMS-1719620 and DMS-2009689.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ren-Cang Li.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, L., Zhang, LH. & Li, RC. Maximizing sum of coupled traces with applications. Numer. Math. 152, 587–629 (2022). https://doi.org/10.1007/s00211-022-01322-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00211-022-01322-y

Keywords

Mathematics Subject Classification

Navigation