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Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
We present in this paper a variation of the self-organizing map algorithm where the original time-dependent (learning rate and neighborhood) learning function is replaced by a time-invariant one. The resulting self-organization does not fit the magnification law and the final vector density is not directly proportional to the density of the distribution. This lead us to introduce the notion of motivated...
Planar projections, i.e. projections from a high dimensional data space onto a two dimensional plane, are still in use to detect structures, such as clusters, in multivariate data. It can be shown that only the subclass of focusing projections such as CCA, NeRV and the ESOM are able to disentangle linear non separable data. However, even these projections are sometimes erroneous. U-matrix methods...
Kernel based learning is very popular in machine learning but often quite costly with at least quadratic runtime complexity. Random Fourier features and related techniques have been proposed to provide an explicit kernel expansion such that standard techniques with low runtime and memory complexity can be used. This strategy leads to rather high dimensional datasets which is a drawback in many cases...
We present a SOM model based on a continuous energy function derived from the original energy-based model developed in [9]. Due to the convolution that is contained in the energy function, this model can only be applied when periodic boundary conditions are imposed (toroidal SOM), leading to markedly higher quantization errors, especially for small map sizes. We introduce a simple strategy, based...
In this contribution, we consider the classification of time-series and similar functional data which can be represented in complex Fourier coefficient space. We apply versions of Learning Vector Quantization (LVQ) which are suitable for complex-valued data, based on the so-called Wirtinger calculus. It makes possible the formulation of gradient based update rules in the framework of cost-function...
In the framework of agnostic learning, one of the main open problems of the theory of multi-category pattern classification is the characterization of the way the confidence interval of a guaranteed risk should vary as a function of the fundamental parameters which are the sample size m and the number C of categories. This is especially the case when working under minimal learnability hypotheses....
This paper uses SOMbrero visualizations to examine two socio-economic dimensions of European states, generated by a factor analysis of time-series data from 2001–2013. We analyze SOMs for 41 countries with regard to “Old Capital” and “New Capital”, two factors that are generated from 12 variables. SOMbrero reveals evidence of various convergence paths over time for these two factors. This approach...
We extend the self-organizing mapping algorithm to the problem of visualizing data on Grassmann manifolds. In this setting, a collection of k points in n-dimensions is represented by a k-dimensional subspace, e.g., via the singular value or QR-decompositions. Data assembled in this way is challenging to visualize given abstract points on the Grassmannian do not reside in Euclidean space. The extension...
Geochemical analyses can provide multiple analytical variables. Accordingly, the generation of large geochemical databases enables imputation studies or analytical estimates of missing values or complex measuring. The processing of bauxite is a key step in the production of aluminum, in which the determination of Reactive Silica (RxSiO2) and Available Alumina (AvAl2O3) are very relevant. The traditional...
This paper describes the filtering effects on classification performance with applying significance degree by SOM to the image analysis using filter bank preprocessing and Subspace Classifier. In our proposed method, a series of analysis concerning accuracy were first conducted in the cases of single filter and filter bank, and examinations on significance degree by SOM were conducted based on green(G)...
In this article we propose a relational and a median possibilistic clustering method. Both methods are modifications of Possibilistic Fuzzy C-Means as introduced by Pal et al. [1]. The proposed algorithms are applicable for abstract non-vectorial data objects where only the dissimilarities of the objects are known. For the relational version we assume a Euclidean data embedding. For data where this...
A patient-specific seizure detection system for Nocturnal Frontal Lobe Epilepsy (NFLE) is proposed. Data of several patients affected by NFLE, extracted from the EPILEPSIAE database, have been used for this study. As every patient possesses different physiological characteristics, several simulations were performed in order to find the best features to be extracted from electroencephalogram (EEG)...
The increase of the computer power has contributed significantly to the development of the Deep Neural Networks. However, the training phase is more difficult since there are many hidden layers with many connections. The aim of this paper is to improve the learning procedure for Deep Neural Networks. A new method for building an evolutionary DNN is presented. With our method, the user does not have...
In this work we report the results of a comprehensive study involving the application of kernel self-organizing maps (KSOM) for early detection of interturn short-circuit faults in a three-phase converter-fed induction motor. For this purpose, two paradigms for developing KSOM-based classifiers are evaluated on the problem of interest, namely the gradient descent based KSOM (GD-KSOM) and the energy...
Self-Organizing Maps (SOM) [ ] are a popular clustering and visualization algorithm. Several implementations of the SOM algorithm exist in different mathematical/statistical softwares, the main one being probably the SOM Toolbox [2]. In this presentation, we will introduce an R package, SOMbrero, which implements several variants of the stochastic SOM algorithm. The package includes several diagnosis...
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