PROPRE is a generic and semi-supervised neural learning paradigm that extracts meaningful concepts of multimodal data flows based on predictability across modalities. It consists on the combination of two computational paradigms. First, a topological projection of each data flow on a self-organizing map (SOM) to reduce input dimension. Second, each SOM activity is used to predict activities in ...

We discuss the importance of finding the correct model complexity, or regularization level, in the self-organizing map (SOM) algorithm. The complexity of the SOM is determined mainly by the width of the final neighborhood, which is usually chosen ad hoc or set to zero for optimal quantization error. However, if the SOM is used for visualizing the joint probability distribution of the data, then...

The Self-Organizing Map (SOM) is one of the popular Artificial Neural Networks which is a useful in clustering and visualizing complex high dimensional data. Conventional SOMs are based on the two-dimensional (2D) grid structure, which usually results in less accurate representation of the data. Several SOMs using spherical data structures have been proposed to remove the “border effect”. In th...

Unsupervised learning scheme like the self-organizing map (SOM) has been used to classify speech sounds in an ordered manner. SOM is able to extract the most salient features of the input signal and provides a simple way of visualizing them. The distance between two units on the map was used as an objective measure of their perceptual similarity. This paper presents a study of the evaluation of...

A self-organizing map (SOM) is an artificial neural network algorithm that can learn from the training data consisting of objects expressed as vectors and perform non-hierarchical clustering to represent input vectors into discretized clusters, with vectors assigned to the same cluster sharing similar numeric or alphanumeric features. SOM has been used widely in transcriptomics to identify co-e...

Information overload is a critical problem in World Wide Web. Kohonen's self-organizing map (SOM) has been proven to be a promising browsing tool for the Web. The SOM algorithm automatically categorizes a large Internet information space into manageable sub-spaces. However, as the amount of information increases, it is expected to increase the size of the map accordingly in order to accommodate...

The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units need to be grouped, i.e., clustered. In t...

For a solution of the visual correspondence problem we have modified the Self Organizing Map (SOM) to map image planes onto another in a neighborhood- and feature-preserving way. We have investigated the convergence speed of this SOM and Dynamic Link Matching (DLM) on a benchmark problem for the solution of which both algorithms are good candidates. We show that even after careful parameter adj...

The Self-Organizing Map (SOM) is a popular and well-studied unsupervised learning technique. Much work has been done recently on visualizing the results of the SOM algorithm, using static non-interactive approaches. This paper presents two new SOM visualization methods, based on the grand tour and linked brushing. These new methods use animation to show progress of the algorithm in the input sp...