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Principal Subspace Flows Via Mechanical Systems on Grassmann Manifolds

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Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 366))

Abstract

We propose a class of second order mechanical systems on Grassmann manifolds that converge to the dominant eigenspace of a given symmetric matrix. Such second order flows for principal subspace analysis are derived from a modification of the familiar Euler-Lagrange equation by inserting a suitable damping term. The kinetic energy of the system is defined by the Riemannian metric on the Grassmannian while the potential energy is given by a trace function, that is defined by the symmetric matrix. Convergence of the algorithm to the dominant subspace is shown for generic initial conditions and a comparison with the Oja flow is made.

Partially supported by the German Research Foundation under grant KONNEW HE 1858/10-1

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References

  1. F. Alvarez, H. Attouch, J. Bolte, and P. Redont. A second-order gradient-like dissipative dynamical system with hessian driven damping. J. MPA, 19:595–603, 2002.

    Google Scholar 

  2. F. Alvarez, J. Bolte, and O. Brahic. Hessian Riemannian gradient flows in convex programming. SIAM J. Control and Opt., 43:477–501, 2004.

    Article  MATH  MathSciNet  Google Scholar 

  3. H. Attouch and P. Redont. The second-order in time continuous Newton methods. SIAM J. Control and Opt., 19:595–603, 1981.

    Article  Google Scholar 

  4. A. Benveniste, M. Metivier, and P. Priouret. Adaptive Algorithms and Stochastic Approximations. Springer Publ., Berlin, 1990.

    MATH  Google Scholar 

  5. A. Bloch, P.S. Krishnaprasad, J.E. Marsden, and T.S. Ratiu. Dissipation induced instabilities. Ann. Inst. H. Poincare, Analyse Nonlineare, 11:37–90, 1994.

    MATH  MathSciNet  Google Scholar 

  6. A. Bloch, P.S. Krishnaprasad, J.E. Marsden, and T.S. Ratiu. The Euler-Poincare equations and double bracket dissipation. Comm. Math. Physics, 175:1–42, 1996.

    Article  MATH  MathSciNet  Google Scholar 

  7. R.W. Brockett. Differential geometry and the design of algorithms. Proc. Symp. Pure Math., 54:69–92, 1993.

    MathSciNet  Google Scholar 

  8. R.W. Brockett. Oscillatory descent for function minimization. In B.H.M. Alber and J. Rosenthal, editors, Current and Future Directions in Applied Mathematics, pages 65–82. Birkhäuser, Boston, 1997.

    Google Scholar 

  9. K.I. Diamantaras and S. Kung. Principal Component Neural Networks. J. Wiley, New York, 1996.

    MATH  Google Scholar 

  10. U. Helmke and P.S. Krishnaprasad. Second order dynamics for principal component analysis. 3rd IFAC Workshop on Lagrangian and Hamiltonian Methods for Nonlinear Control, Preprints, Nagoya, 2006.

    Google Scholar 

  11. U. Helmke and J. Moore. Optimization and Dynamical Systems. Springer, London, 1994.

    Google Scholar 

  12. C. Lageman. Convergence of gradient-like flows. submitted to Math. Nachrichten, 2006.

    Google Scholar 

  13. E. Oja. A simplified neuron model as a principal component analyzer. J. Math. Biology, 15:267–273, 1982.

    Article  MATH  MathSciNet  Google Scholar 

  14. E. Oja. Neural networks, principal components, and subspaces. Int. J. Neural Systems, 1:61–68, 1989.

    Article  MathSciNet  Google Scholar 

  15. S. Yoshizawa, U. Helmke, and K. Starkov. Convergence analysis for principal component flows. Int. J. Appl. Math. Comput. Sci., 11:223–236, 2001.

    MathSciNet  Google Scholar 

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Helmke, U., Krishnaprasad, P. (2007). Principal Subspace Flows Via Mechanical Systems on Grassmann Manifolds. In: Allgüwer, F., et al. Lagrangian and Hamiltonian Methods for Nonlinear Control 2006. Lecture Notes in Control and Information Sciences, vol 366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73890-9_20

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  • DOI: https://doi.org/10.1007/978-3-540-73890-9_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73889-3

  • Online ISBN: 978-3-540-73890-9

  • eBook Packages: EngineeringEngineering (R0)

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