JOURNAL OF COMPUTERS (JCP)
ISSN : 1796-203X
Volume : 3    Issue : 9    Date : September 2008

Relation Organization of SOM Initial Map by Improved Node Exchange
Tsutomu Miyoshi
Page(s): 77-84
Full Text:
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Abstract
The Self Organizing Map (SOM) involves neural networks, that learns the features of input data
thorough unsupervised, competitive neighborhood learning. In the SOM learning algorithm,
connection weights in a SOM feature map are initialized at random values, which also sets nodes at
random locations in the feature map independent of input data space. The move distance of output
nodes increases, slowing learning convergence. As precedence research, we proposed the
method to improve this problem, initial node exchange by using a part of feature map. In this paper,
we propose two improved exchange method, node exchange with fixed neighbor area and spiral
node exchange. The node exchange with fixed neighbor area uses fixed position of winner node
and fixed initial size of neighbor area that sets to cover whole feature map. We investigate how
average move distance of all nodes and average deviation of move distance would change with the
differences by type of fixed neighbor area in node exchange process. The spiral node exchange is
used instead of neighbor area reduction reputation of former method. By spiral node exchange,
repetition by node exchange process becomes needless and can expect speed up of total
processing.

Index Terms
first Self-organizing map, feature map, node exchange, fixed neighbor area, spiral exchange.