• DocumentCode
    436591
  • Title

    A new model selection criterion based on information geometry

  • Author

    Yunhui, Liu ; Siwei, Luo ; Aijun, Li ; Hanbin, Yu

  • Author_Institution
    Dept. of Comput. Sci., Beijing Jiao Tong Univ., China
  • Volume
    2
  • fYear
    2004
  • fDate
    31 Aug.-4 Sept. 2004
  • Firstpage
    1562
  • Abstract
    This paper presents a new model selection criterion based on information geometry - Information Geometric Model Selection Criterion (IGMSC) which is reparametrization invariant, and gives the proof. IGMSC computes the geometric complexity of the model by regarding the model space as the manifold and estimates the model-data geometric fitness by using the divergence between the true distribution and the asymptotic distribution, enduing complexity and fitness with clear geometric significance. IGMSC gives the theoretic support of model selection in the framework of information geometry.
  • Keywords
    computational geometry; learning (artificial intelligence); statistical distributions; IGMSC; geometric complexity; geometric fitness; information geometry; model selection criterion; Bayesian methods; Information geometry; Large-scale systems; Length measurement; Machine learning; Neural networks; Predictive models; Probability distribution; Solid modeling; Stochastic processes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing, 2004. Proceedings. ICSP '04. 2004 7th International Conference on
  • Print_ISBN
    0-7803-8406-7
  • Type

    conf

  • DOI
    10.1109/ICOSP.2004.1441627
  • Filename
    1441627