DocumentCode :
3643616
Title :
Maximization of the information divergence from an exponential family and criticality
Author :
František Matúš;Johannes Rauh
Author_Institution :
Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Pod vodá
fYear :
2011
fDate :
7/1/2011 12:00:00 AM
Firstpage :
903
Lastpage :
907
Abstract :
The problem to maximize the information divergence from an exponential family is compared to the maximization of an entropy-like quantity over the boundary of a polytope. First-order conditions on directional derivatives define critical sets for the two problems. The bijection between the sets of global maximizers in the two problems found earlier is extended here to bijections between the sets of local maximizers and the critical sets. This is based on new inequalities relating the maximized quantities and a reformulation of the first order criticality conditions for the second problem.
Keywords :
"Atmospheric measurements","Particle measurements","Information theory","Vectors","Neural networks","Calculus"
Publisher :
ieee
Conference_Titel :
Information Theory Proceedings (ISIT), 2011 IEEE International Symposium on
ISSN :
2157-8095
Print_ISBN :
978-1-4577-0596-0
Electronic_ISBN :
2157-8117
Type :
conf
DOI :
10.1109/ISIT.2011.6034269
Filename :
6034269
Link To Document :
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