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
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