Methods for visualizing multidimensional data are of great interest in computer science and engineering. One popular technique is selforganizing map, a type of neural network, that uses machine learning algorithms to map multidimensional data to a two-dimensional surface. They are widely used for exploratory data analysis and visualization and have been used to perform clustering and classifica...

To execute a parallel program on a multicomputer system, the tasks of the program have to be mapped to the particular processors of the parallel machine. To keep communication delays low, communicating tasks should be placed closely together. Since both the communication structure of the program and the interconnection structure of the parallel machine can be represented as graphs, the mapping ...

The Self-Organizing Map (SOM), and other related architectures, enjoy a growing popularity in the field of Data Mining. These neural network algorithms provide a topology-preserving mapping from high-dimensional data to a lower dimension, which allows for an easier interpretation of complex data. For visualisation of trained maps, a lot of different techniques have been developed. However, conv...

New−generation SAR missions will provide polarimetric data, that, besides other features, have the potential of improving the accuracy of land cover mapping. Processing of polarimetric data for classification purposes has been carried out by a variety of algorithms which span from Bayesian Maximum Likelihood to Fuzzy Logic to Support Vector Machines to Neural Networks. Target decomposition prov...

Kohonen's Self-Organizing Map (SOM) is one of the most popular arti cial neural network algorithms. Word category maps are SOMs that have been organized according to word similarities, measured by the similarity of the short contexts of the words. Conceptually interrelated words tend to fall into the same or neighboring map nodes. Nodes may thus be viewed as word categories. Although no a prior...

We review a recent extension of the self-organizing map (SOM) for temporal structures with a simple recurrent dynamics leading to sparse representations, which allows an efficient training and a combination with arbitrary lattice structures. We discuss its practical applicability and its theoretical properties. Afterwards, we put the approach into a general framework of recurrent unsupervised m...

The self-organizing map (SOM) algorithm for finite data is derived as an approximate MAP estimation algorithm for a Gaussian mixture model with a Gaussian smoothing prior, which is equivalent to a generalized deformable model (GDM). For this model, objective criteria for selecting hyperparameters are obtained on the basis of empirical Bayesian estimation and crossvalidation, which are represent...

Models are abstractions of observed real world phenomena or processes. A good model captures the essential properties of the modeled phenomena. In the statistical learning paradigm the processes that generate observations are assumed unknown and too complex for analytical modeling, thus the models are trained from more general templates with measured observations. A substantial part of the proc...

Self-organizing maps (SOMs) are widely used in several fields of application, from neurobiology to multivariate data analysis. In that context, this paper presents variants of the classic SOM algorithm. With respect to the traditional SOM, the modifications regard the core of the algorithm, (the learning rule), but do not alter the two main tasks it performs, i.e. vector quantization combined w...