DocumentCode
1142289
Title
A note on self-organizing semantic maps
Author
Bezdek, James C. ; Pal, Nikhil R.
Author_Institution
Dept. of Comput. Sci., West Florida Univ., Pensacola, FL, USA
Volume
6
Issue
5
fYear
1995
fDate
9/1/1995 12:00:00 AM
Firstpage
1029
Lastpage
1036
Abstract
This paper discusses Kohonen´s self-organizing semantic map (SOSM). We show that augmentation and normalization of numerical feature data as recommended for the SOSM is entirely unnecessary to obtain semantic maps that exhibit semantic similarities between objects represented by the data. Visual displays of a small data set of 13 animals based on principal components, Sammon´s algorithm, and Kohonen´s (unsupervised) self-organizing feature map (SOFM) possess exactly the same qualitative information as the much more complicated SOSM display does
Keywords
feature extraction; object recognition; self-organising feature maps; semantic networks; Kohonen self-organizing feature map; Sammon´s algorithm; augmentation; feature extraction; normalization; principal components; self-organizing semantic maps; Algorithm design and analysis; Animals; Computer science; Data mining; Displays; Feature extraction; Linearity; Pixel; Scattering; Stock markets;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.410347
Filename
410347
Link To Document