SOMs for Machine Learning

In this chapter we offer a survey of self-organizing feature maps with emphasis on recent advances, and more specifically, on growing architectures. Several of the methods are developed by the authors and offer unique combination of theoretical fundamentals and neural network architectures. Included in this survey of dynamic architectures, will also be examples of application domains, usage and resources for learners and researchers alike, to pursue their interest in SOMs. The primary reason for pursuing this branch of machine learning, is that these techniques are unsupervised – requiring no a priori knowledge or trainer. As such, SOMs lend themselves readily to difficult problem domains in machine learning, such as clustering, pattern identification and recognition and feature extraction. SOMs utilize competitive neural network learning algorithms introduced by Kohonen in the early 1980’s. SOMs maintain the features (in terms of vectors) of the input space the network is observing. This chapter, as work emphasizing dynamic architectures, will be incomplete without presenting the significant achievements in SOMs including the work of Fritzke and his growing architectures. To exemplify more modern approaches we present state-of-the art developments in SOMs. These approaches include parallelization (ParaSOM – as developed by the authors), incremental learning (ESOINN), connection reorganization (TurSOM – as developed by the authors), and function space organization (mnSOM). Additionally, we introduce some methods of analyzing SOMs. These include methods for measuring the quality of SOMs with respect to input, neighbors and map size. We also present techniques of posterior recognition, clustering and input feature significance. In summary, this chapter presents a modern gamut of self-organizing neural networks, and measurement and analysis techniques.