• DocumentCode
    2705960
  • Title

    Learning averages over the lie group of symmetric positive-definite matrices

  • Author

    Fiori, Simone ; Tanaka, Toshihisa

  • Author_Institution
    Dipt. di Ing. Biomedica, Elettron. e Telecomun., Univ. Politec. delle Marche, Ancona, Italy
  • fYear
    2009
  • fDate
    14-19 June 2009
  • Firstpage
    828
  • Lastpage
    832
  • Abstract
    In the present paper, we treat the problem of learning averages out of a set of symmetric positive-definite matrices (SPDMs). We discuss a possible learning technique based on the differential geometrical properties of the SPDM-manifold which was recently shown to possess a Lie-group structure under appropriate group definition. We first recall some relevant notions from differential geometry, mainly related to Lie-group theory, and then we propose a scheme of learning averages. Some numerical experiments will serve to illustrate the features of the learnt averages.
  • Keywords
    Lie groups; computational complexity; differential geometry; estimation theory; group theory; learning (artificial intelligence); matrix algebra; Lie group theory; SPDM manifold; differential geometry; learning average; symmetric positive definite matrix; Automatic control; Biomedical engineering; Biomedical measurements; Biomedical signal processing; Covariance matrix; Humans; Intelligent control; Robotics and automation; Symmetric matrices; Tensile stress;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2009. IJCNN 2009. International Joint Conference on
  • Conference_Location
    Atlanta, GA
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-3548-7
  • Electronic_ISBN
    1098-7576
  • Type

    conf

  • DOI
    10.1109/IJCNN.2009.5178598
  • Filename
    5178598