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
fDate :
9/1/1995 12:00:00 AM
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;
Journal_Title :
Neural Networks, IEEE Transactions on
