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
Link To Document :
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