NDLI: Structural enhanced information and its application to improved visualization of self-organizing maps

Content Provider	Springer Nature Link
Author	Kamimura, Ryotaro
Copyright Year	2009
Abstract	In this paper, we propose structural enhanced information for detecting and visualizing main features in input patterns. We have so far proposed information enhancement for feature detection, where, if we want to focus upon components such as units and connection weights and interpret the functions of the components, we have only to enhance competitive units with the components. Though this information enhancement has given favorable results in feature detection, we further refine the information enhancement and propose structural enhanced information. In structural enhanced information, three types of enhanced information can be differentiated, that is, first-, second- and third-order enhanced information. The first-order information is related to the enhancement of competitive units themselves in a competitive network, and the second-order information is dependent upon the enhancement of competitive units with input patterns. Then, the third-order information is obtained by subtracting the effect of the first-order information from the second-order information. Thus, the third-order information more explicitly represents information on input patterns. With this structural enhanced information, we can estimate more detailed features in input patterns. For demonstrating explicitly and intuitively the improved performance of our method, the conventional SOM was used, and we transformed competitive unit outputs so as to improve visualization. The method was applied to the well-known Iris problem, an OECD countries classification problem and the Johns Hopkins University Ionosphere database. In all these problems, we succeeded in visualizing the detailed and important features of input patterns by using the third-order information.
Starting Page	102
Ending Page	115
Page Count	14
File Format	PDF
ISSN	0924669X
Journal	Applied Intelligence
Volume Number	34
Issue Number	1
e-ISSN	15737497
Language	English
Publisher	Springer US
Publisher Date	2009-06-04
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Competitive learning SOM Mutual information Enhanced information Structural enhanced information Manufacturing, Machines, Tools Mechanical Engineering Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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