The Self-Organizing Maps: Background, Theories, Extensions and Applications

  title={The Self-Organizing Maps: Background, Theories, Extensions and Applications},
  author={Hujun Yin},
  booktitle={Computational Intelligence: A Compendium},
  • Hujun Yin
  • Published in
    Computational Intelligence: A…
  • Computer Science
For many years, artificial neural networks (ANNs) have been studied and used to model information processing systems based on or inspired by biological neural structures. They not only can provide solutions with improved performance when compared with traditional problem-solving methods, but also give a deeper understanding of human cognitive abilities. Among various existing neural network architectures and learning algorithms, Kohonen’s selforganizing map (SOM) [46] is one of the most popular… 

A Fast Algorithm to Find Best Matching Units in Self-Organizing Maps

A faster alternative to compute the Winner Takes All component of SOM that scales better with a large number of neurons is proposed that can be combined with other optimization methods commonly used in these models for an even faster computation in both learning and recall phases.

Topology-based analysis of self-organizing maps for time series prediction

The impact of the number of neurons, the effect of the best-matching unit over its neighborhood, the use of nonlinear learning rate functions, and the importance of a proportional training together with a sampled input space are studied as uniformly as possible.

Extended Self Organizing Maps for Structured Domain: Models and Learning

This work proposes four models of extended Self Organizing Maps (SOM) that can be applied to graph data structures as input and output domains together with learning algorithms.

NP-SOM: Network Programmable Self-Organizing Maps

A model, namely the NP-SOM (network programmable self-organizing map), able to define SOMs with different underlying topologies as the result of a specific configuration of the associated NoC is developed.

Enhanced data clustering and classification using auto-associative neural networks and self organizing maps

It can be concluded that both methodologies have been able to improve data clustering and classification performance as well as to discover inherent information inside multidimensional data.

Self-Organizing Map Formation with a Selectively Refractory Neighborhood

A modification to the SOM algorithm is introduced in which neighborhood is contemplated from the point of view of affected units, not from the view of BMUs, and the maps achieved have, in many cases, a lower error measure than the maps formed by SOM.

The role of the lattice dimensionality in the self-organizing map

A theory of this kind is developed, which can be used to assess which topologies are better suited for vector quantization and shows that the 1D maps perform significantly better in many cases, which agrees with the theoretical study.

Clustering and Visualizing SOM Results

This paper presents a gradient-based SOM visualization method and compares it with U-matrix and proposes an enhancing method to map visualization taking advantage of the neurons activity, which improve cluster detection especially in small maps.



Self-Organising Maps for Pattern Recognition

Adaptive, associative, and self-organizing functions in neural computing.

This paper contains an attempt to describe certain adaptive and cooperative functions encountered in neural networks, to reason what functions are readily amenable to analytical modeling and which phenomena seem to ensue from the more complex interactions that take place in the brain.

The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data

The motivation was to provide a model that adapts its architecture during its unsupervised training process according to the particular requirements of the input data, and by providing a global orientation of the independently growing maps in the individual layers of the hierarchy, navigation across branches is facilitated.

Self-Organizing Neural Networks for Visualisation and Classification

It has been demonstrated, that the usage of an artificial neural network, Kohonen’s self organizing feature map, for visualisation and classification of high dimensional data, can be used also for knowledge acquisition and exploratory data analysis purposes.

Generalizing self-organizing map for categorical data

A generalized self-organizing map model is proposed that offers an intuitive method of specifying the similarity between categorical values via distance hierarchies and, hence, enables the direct process of categoricalvalues during training.

Representation Of Sensory Information In Self-Organizing Feature Maps, And Relation Of These Maps To Distributed Memory Networks

This paper contains some new results which show that both of the above functions, viz. formation of the internal representations and their storage, can be implemented simultaneously by an adaptive, massively parallel, self-organizing network.

Recursive self-organizing maps

Self-organizing maps: ordering, convergence properties and energy functions

It is proved that the learning dynamics cannot be described by a gradient descent on a single energy function, but may be described using a set of potential functions, one for each neuron, which are independently minimized following a stochastic gradient descent.

Self-organization of orientation sensitive cells in the striate cortex

A nerve net model for the visual cortex of higher vertebrates is presented. A simple learning procedure is shown to be sufficient for the organization of some essential functional properties of