DocumentCode :
3455583
Title :
Full Conditional Free Energy Based Inference
Author :
Chen, Feng ; Cheng, Qiang ; Liu, Hong ; Xu, Wenli
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing, China
fYear :
2010
fDate :
21-23 Oct. 2010
Firstpage :
1
Lastpage :
5
Abstract :
Inference on graphical models has great applications in the fields such as pattern recognition, artificial intelligence and statistics. The inference problem is usually studied as an optimization problem w.r.t. free energy, such as Bethe/Kikuchi free energy minimization. However, due to the nonconvexity of these free energies, it is often infeasible to obtain the global optimum. In this paper, we propose a new inference approach that can obtain the global optimum. Subsequently, we interpret this approach in terms of minimizing a new free energy, full conditional free energy (FCFE). Based on FCFE, approximate FCFE and an efficient approximate algorithm are proposed. Finally, experiments show the efficiency of the inference framework.
Keywords :
inference mechanisms; optimisation; Bethe-Kikuchi free energy minimization; artificial intelligence; full conditional free energy; graphical models; inference; optimization problem; pattern recognition; statistics; w.r.t. free energy; Approximation algorithms; Approximation methods; Equations; Graphical models; Inference algorithms; Markov processes; Mathematical model;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (CCPR), 2010 Chinese Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-7209-3
Electronic_ISBN :
978-1-4244-7210-9
Type :
conf
DOI :
10.1109/CCPR.2010.5659129
Filename :
5659129
Link To Document :
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