Kohonen's self-organizing map bears large potentials as a universal tool of nonlinear data analysis. From the practical point of view control parameters like the learning rate and the neighborhood width need special attention in order to exploit the possibilities of the approach. Our paper introduces second order learning methods which generalize the dynamics of Kohonen's learning algorithm in ...

The original Kohonen's Self-Organizing Map model has been extended by several authors to incorporate an underlying probability distribution. These proposals assume mixtures of Gaussian probability densities. Here we present a new self-organizing model which is based on a mixture of multivariate Student-t components. This improves the robustness of the map against outliers, while it includes the...

SFI WORKING PAPER: 1998-07-066 SFI Working Papers contain accounts of scientific work of the author(s) and do not necessarily represent the views of the Santa Fe Institute. We accept papers intended for publication in peer-reviewed journals or proceedings volumes, but not papers that have already appeared in print. Except for papers by our external faculty, papers must be based on work done at ...

Clustering time series has become in recent years a topic of great interest in a wide range of fields. The several approaches differ mainly in their notion of similarity (for a review see Focardi, 2001). Most researches use the Euclidean distance or some variation of it because of its easy implementation, even if it is very sensitive to temporal axis alignment. Furthermore, there are many appli...

A major problem in connectionist phonetic acoustic decoding is the way to present acoustic signal to the network. Neurobiological data about the inner ear and the primary auditory cortex can be very helpful but are rare. On the other hand, other biological works have shown that structure and functioning of the visual cortex, which have been extensively studied, are very close to the structure a...

In recent years a great amount of research has focused on algorithms that learn features from unlabeled data. In this work we propose a model based on the Self-Organizing Map (SOM) neural network to learn features useful for the problem of automatic natural images classification. In particular we use the SOM model to learn single-layer features from the extremely challenging CIFAR-10 dataset, c...

As an active research area, face recognition has been studied for more than 20 years. Especially, after the September 11 terrorist attacks on the United States, security systems utilizing personal biometric features, such as, face, voice, fingerprint, iris pattern, etc. are attracting a lot of attention. Among them, face recognition systems have become the subject of increased interest (Bowyer,...

Content-based video navigation is an efficient method for browsing video information. A common approach is to cluster shots into groups and visualize them afterwards. In this paper, we present a prototype that follows in general this approach. The clustering ignores temporal information and is based on a growing self-organizing map algorithm. They provide some inherent visualization properties ...

With the WEBSOM method a textual document collection may be organized onto a graphical map display that provides an overview of the collection and facilitates interactive browsing. Interesting documents can be located on the map using a content-directed search. Each document is encoded as a histogram of word categories which are formed by the self-organizing map (SOM) algorithm based on the sim...