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A long-standing problem in the field of connectionist language processing has been how to represent detailed linguistic structure. Approaches have ranged from the encoding of syntactic trees in Raam to the use of a mechanism to query meanings in a “gestalt layer”. In this article, a technique called semantic self-organization is presented that allows for the optimal allocation and explicit representation...
SOMs have proven to be a very powerful tool for data analysis. However, comparing multiple SOMs trained on the same data set using different parameters or initialisations is still a difficult task. In most cases it is performed only via visual inspection or by utilising one of a range of quality measures to compare vector quantisation or topology preservation characteristics of the maps. Yet, comparing...
Lexicon is a central component in any language processing system, whether human or artificial. Recent empirical evidence suggests that a multilingual lexicon consists of a single component representing word meanings, and separate component for the symbols in each language. These components can be modeled as self-organizing maps, with associative connections between them implementing comprehension...
The training scheme in self-organizing maps consists of two phases: i) competition, in which all units intend to become the best matching unit (BMU), and ii) cooperation, in which the BMU allows its neighbor units to adapt their weight vector. In order to study the relevance of cooperation, we present a model in which units do not necessarily cooperate with their neighbors, but follow some strategy...
The complex phenomena of political science are typically studied using qualitative approach, potentially supported by hypothesis-driven statistical analysis of some numerical data. In this article, we present a complementary method based on data mining and specifically on the use of the self-organizing map. The idea in data mining is to explore the given data without predetermined hypotheses. As a...
A method for generating a self-organizing map of line images is proposed. In the proposed method, called the NG×SOM, a set of data distributions is represented by a product space organized by a set of neural gas networks (NGs) and a self-organizing map (SOM). In this paper, it is assumed that the line images dealt with by the NG×SOM have the same, yet unknown, topology. Thus the task of the NG×SOM...
In our earlier work, we found that feature space induced by tactile receptive fields (TRFs) are better than that by visual receptive fields (VRFs) in texture boundary detection tasks. This suggests that TRFs could be intimately associated with texture-like input. In this paper, we investigate how TRFs can develop in a cortical learning context. Our main hypothesis is that TRFs can be self-organized...
We apply a recent formalization of visualization as information retrieval to linear projections. We introduce a method that optimizes a linear projection for an information retrieval task: retrieving neighbors of input samples based on their low-dimensional visualization coordinates only. The simple linear projection makes the method easy to interpret, while the visualization task is made well-defined...
Topographic maps are an appealing exploratory instrument for discovering new knowledge from databases. During the recent years, several variations on the Self Organizing Maps (SOM) were introduced in the literature. In this paper, the toroidal Emergent SOM tool and the spherical SOM are used to analyze a text corpus consisting of police reports of all violent incidents that occurred during the first...
Monitoring volcanic activity is a task that requires people from a number of disciplines. Infrastructure, on the other hand , has been built all over the world to keep track of these living earth entities, ie volcanoes. In this paper we present an approach that merges a number of computational tools and that may be incorporated to existing ones to predict important volcanic events. It mainly consists...
We propose a method to eliminate unnecessary neurons in Variable-Density Self-Organizing Map. We have defined an energy function which denotes the error of the map, and optimize the energy function by using graph cut algorithm. We conducted experiments to investigate the effectiveness of our approach.
In this paper we present an extended version of Evolving Trees using Oja’s rule. Evolving Trees are extensions of Self-Organizing Maps developed for hierarchical classification systems. Therefore they are well suited for taxonomic problems like the identification of bacteria. The paper focus on clustering and visualization of bacteria measurements. A modified variant of the Evolving Tree is developed...
The distribution of a class of objects, such as images depicting a specific topic, can be studied by observing the best-matching units (BMUs) of the objects’ feature vectors on a Self-Organizing Map (SOM). When the BMU “hits” on the map are summed up, the class distribution may be seen as a two-dimensional histogram or discrete probability density. Due to the SOM’s topology preserving property, one...
This paper presents a new methodology for missing value imputation in a database. The methodology combines the outputs of several Self-Organizing Maps in order to obtain an accurate filling for the missing values. The maps are combined using MultiResponse Sparse Regression and the Hannan-Quinn Information Criterion. The new combination methodology removes the need for any lengthy cross-validation...
We explore generic mechanisms to introduce structural hints into the method of Unsupervised Kernel Regression (UKR) in order to learn representations of data sequences in a semi-supervised way. These new extensions are targeted at representing a dextrous manipulation task. We thus evaluate the effectiveness of the proposed mechanisms on appropriate toy data that mimic the characteristics of the aimed...
Among the popular lifestyle-related diseases are smoking, overweight and stress. A daily health check is important because there is no clear objective symptom for these diseases. We developed diagnotic software which shows the state of the blood vessels using a Basic SOM model, and performs synthetic plethysmogram analysis of 4 components using the map location (the state of the blood vessel, vascularity),...
This paper discusses biological aspects of self-organising maps (SOMs) which includes a brief review of neurophysiological findings and classical models of neurophysiological SOMs. We then discuss some simulation studies on the role of topographic map representation for training mapping networks and on top-down control of map plasticity.
In this paper we present a method for functional principal component analysis based on the Oja-learning and neural gas vector quantizer. However, instead of the Euclidean inner product the Sobolev counterpart is applied, which takes the derivatives of the functional data into account and, therefore, uses information contained in the functional shape of the data into account. We investigate the theoretical...
We introduce the Exploration Machine (Exploratory Observation Machine, XOM) as a novel versatile instrument for scientific data analysis and knowledge discovery. XOM systematically inverts structural and functional components of topology-preserving mappings. In contrast to conventional approaches known from the literature, this novel computational framework for self-organization does not require to...
We present a novel method for structure-preserving dimensionality reduction. The Exploration Machine (Exploratory Observation Machine, XOM) computes graphical representations of high-dimensional observations by a strategy of self-organized model adaptation. Although simple and computationally efficient, XOM enjoys a surprising flexibility to simultaneously contribute to several different domains of...
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