Volume 4 Number 10 (Oct. 2009)
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JCP 2009 Vol.4(10): 943-953 ISSN: 1796-203X
doi: 10.4304/jcp.4.10.943-953

Feature Discovery by Information Loss

Ryotaro Kamimura
IT Education Center, Tokai University, Kanagawa, Japan
Abstract—In this paper, we propose a new approach called information loss to feature detection in competitive learning. The information loss is defined by the difference between a full network and a network without some elements. If this deletion significantly decreases the amount of information contained in a network, the elements are considered to be important and are expected to play a very important role. The method was applied to artificial and symmetric data to show the features extracted by the information loss. Then, we applied the method to the classification of OECD countries. The experimental results confirmed that the method was efficient enough to detect main features comparable to those detected by the conventional SOM.

Index Terms—Mutual information, information loss, feature detection, competitive learning, self-organizing maps.

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Cite: Ryotaro Kamimura, "Feature Discovery by Information Loss," Journal of Computers vol. 4, no. 10, pp. 943-953, 2009.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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