ISSN : 1796-203X
Volume : 4    Issue : 10    Date : October 2009

Feature Discovery by Information Loss
Ryotaro Kamimura
Page(s): 943-953
Full Text:
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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