This paper describes an improvement of the self-organizing feature map (SOFM) obtained with the Kohonen neu-ral network. The ameliorations are dedicated to its usage in computer graphics and mainly in animation of particle-based systems. We show its application in the context of the visualization of molecular dynamics systems. Finally, we compare this solution with other works based on particle...

Keywords: Topology preserving Self-organizing map Growing cell structures Visualization methods Delaunay triangulation The Self-Organizing Map (SOM) is a neural network model that performs an ordered projection of a high dimensional input space in a low-dimensional topological structure. The process in which such mapping is formed is defined by the SOM algorithm, which is a competitive, unsuper...

Due to large datavolumes when remote sensing or other kind of images are used, there is need for methods to decrease the volume of data. Methods for decreasing the feature dimension, in other words number of channels, are called feature selection and feature extraction. In the feature selection, important channels are selected using some search technique and these channels are used for current ...

What, if anything, is the functional significance of spatial patterning in cortical feature maps? We ask this question of four major theories of cortical map formation: self-organizing maps, wiring optimization, place coding, and reaction-diffusion. We argue that i) self-organizing maps yield spatial patterning only as a byproduct of efficient mechanisms for developing environmentally appropria...

Self-organizing maps are artificial neural networks designed for unsupervised machine learning. They represent powerful data analysis tools applied in many different areas including areas such as biomedicine, bioinformatics, proteomics, and astrophysics. We maintain a data analysis package in R based on self-organizing maps. The package supports efficient, statistical measures that enable the u...

A Maximum-likelihood connectionist model for unsupervised learning over graphical domains

In this paper a one-dimension Self Organizing Map algorithm (SOM) to perform feature selection is presented. The algorithm is based on a first classification of the input dataset on a similarity space. From this classification for each class a set of positive and negative features is computed. This set of features is selected as result of the procedure. The procedure is evaluated on an in-house...