Self-Organizing Maps and Their Applications in Image Processing, Information Organization, and Retrieval - Nonlinear Signal and Image Processing: Theory, Methods and Applications - page 387

11

Self-Organizing Maps and Their Applications
in Image Processing, Information
Organization, and Retrieval

Constantine Kotropoulos and Ioannis Pitas

CONTENTS
11.1 Introduction ......................................................... 387

11.2 Basic Feature-Mapping Models ..................................... 389

11.3 Kohonen's Self-Organizing Map .................................... 394

11.4 Convergence Analysis of Self-Organizing Maps .................... 396

11.5 Self-Organizing Map Properties .................................... 401

11.6 Variants of Self-Organizing Maps Based on Robust Statistics ...... 404

11.7 A Class of Split-Merge Self-Organizing Maps ...................... 420

11.8 Conclusions .......................................................... 440

References .................................................................. 440

11.1 Introduction

Neural networks (NNs) are able to learn from their environment so that their
performance is improved. In several NN categories, learning is provided by
a desirable input-output mapping that the NN approximates. This is called
supervised learning .Typical NNs where supervised learning is employed are
called multilayer perceptron or radial-basis functions networks. Another prin-
ciple is the unsupervised learning or self-organized learning that aims at
identifying the important features in the input data without a supervisor.
Unsupervised learning algorithms are equipped with a set of rules that lo-
cally update the synaptic weights of the network. The topologies of NNs that
are trained using unsupervised learning are more similar to neurobiological
structures than are those of NNs that are trained using supervised learning.
The basic topologies of self-organizing NNs are as follows: