Non-logarithmic information measures, α-weighted EM algorithms and speedup of learning

Author

Matsuyama, Yasuo

Author_Institution

Dept. of Electr., Electron. & Comput. Eng., Waseda Univ., Tokyo, Japan

fYear

1998

fDate

16-21 Aug 1998

Firstpage

385

Abstract

Starting from Renyi´s α-divergence, a class of generalized EM algorithms called the α-EM algorithms of the WEM algorithms are derived. Merits of this generalization are found on speedup of learning, i.e., acceleration of convergence. Discussions include novel α-versions of logarithm, efficient scores, information matrices and the Cramer-Rao bound. The speedup is examined on Gaussian mixture learning systems

Keywords

convergence of numerical methods; information theory; optimisation; α-weighted EM algorithms; Cramer-Rao bound; Gaussian mixture learning systems; Renyi´s α-divergence; WEM algorithms; convergence acceleration; generalized EM algorithms; information matrices; learning speedup; nonlogarithmic information measures; Acceleration; Convergence; Electric variables measurement; Electrochemical machining; Entropy; Information theory; Intersymbol interference; Learning systems; Linear matrix inequalities; Velocity measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Theory, 1998. Proceedings. 1998 IEEE International Symposium on

Conference_Location

Cambridge, MA

Print_ISBN

0-7803-5000-6

Type

conf

DOI

10.1109/ISIT.1998.708990

Filename

708990

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2488286