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

@inproceedings{Yin2008TheSM, title={The Self-Organizing Maps: Background, Theories, Extensions and Applications}, author={Hujun Yin}, booktitle={Computational Intelligence: A Compendium}, year={2008} }

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…

## 163 Citations

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

- Computer ScienceICANN
- 2020

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

- Computer ScienceSoft Comput.
- 2017

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

- Computer Science
- 2015

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.

### Adaptive nonlinear manifolds and their applications to pattern recognition

- Computer ScienceInf. Sci.
- 2010

### NP-SOM: Network Programmable Self-Organizing Maps

- Computer Science2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)
- 2018

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

- Computer Science
- 2016

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

- Computer ScienceNeural Processing Letters
- 2013

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.

### Data-Independent Feature Learning with Markov Random Fields in Convolutional Neural Networks

- Computer ScienceNeurocomputing
- 2020

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

- Computer Science
- 2018

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

- Computer ScienceIDEAL
- 2010

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.

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