Self-Organizing Maps and Unsupervised Classification - Neural Networks: Methodology and Applications - page 379

7
Self-Organizing Maps and Unsupervised
Classification
F. Badran, M. Yacoub, and S. Thiria
This chapter is dedicated to the second group of neural networks: Topologi-
cal self-organizing maps. Those models are subject to unsupervised learning,
in contrast with multilayer perceptrons, which were described in previous
chapters. Primarily, the purpose of those models is purely descriptive: some
structure is sought in given data. There is neither precise action to perform,
nor desired response to obtain. Alternatively, information compression can be
considered as the purpose of unsupervised learning: a compact description of
the data, with minimal distortion, is sought.
The unsupervised learning methods that are used by topological self-
organizing maps stemmed from techniques that were first designed for compet-
itive learning. Among pioneering works in the field, one may quote
[Didday 1976] and [von der Malsburg 1973]. The models were made of parallel
filters that analyzed the same observation. For that observation, the filters'
responses were different, and the filter that generated the highest response
was said to win the competition. That winner is then favored by competitive
learning, and the training algorithm enhanced the response of that filter to
that observation. The same operation is performed for all observations of the
training set until stabilization of the parameters of the filters. At that stage,
each filter has been made sensitive to features that are specific to a subset of
thedataset:itoperatesasafeaturedetector.
Topological maps or self-organizing maps were first introduced by T. Ko-
honen in 1981. The first models were designed for processing high-dimensional
data. Very large data sets with high dimensional data vectors were involved
in the applications under consideration. In order to process such data, the
topological map visualization methodology is designed to partition available
data into clusters of data that exhibit some similarity. The training process is
driven by the data set. The specificity of topological maps is to provide the
clusters with a neighborhood structure, which is actually a graph structure on
a discrete set. Low-dimension lattices (1D, 2D or 3D grid) are most frequently
considered.