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Generalized Matrix Learning Vector Quantization (GMLVQ) critically relies on the use of an optimization algorithm to train its model parameters. We test various schemes for automated control of learning rates in gradient-based training. We evaluate these algorithms in terms of their achieved performance and their practical feasibility. We find that some algorithms do indeed perform better than others...
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...
Learning vector quantization (LVQ) enjoys a great popularity as efficient and intuitive classification scheme, accompanied by a strong mathematical substantiation of its learning dynamics and generalization ability. However, popular deterministic LVQ variants do not allow an immediate probabilistic interpretation of its output and an according reject option in case of insecure classifications. In...
Multiple signals are measured by sensors during a flight or a test bench and their analysis represent a big interest for engineers. These signals are actually multivariate time series created by the sensors present on the aircraft engines. Each of them can be decomposed into series of stabilized phases, well known by the experts, and transient phases. Transient phases are merely explored but they...
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...
We present a framework for distance-based classification of functional data. We consider the analysis of labeled spectral data and time series by means of Generalized Matrix Relevance Learning Vector Quantization (GMLVQ) as an example. To take advantage of the functional nature a functional expansion of the input data is considered. Instead of using a predefined set of basis functions for the expansion...
We present in this paper a new approach of supervised self organizing map (SOM). We added a supervised perceptron layer to the classical SOM approach. This combination allows the classification of new patterns by taking into account all the map prototypes without changing the SOM organization. We also propose to associate two reject options to our supervised SOM. This allows to improve the results...
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)...
Social networks are known to be assortative with respect to many attributes, such as age, weight, wealth, level of education, ethnicity and gender. This can be explained by influences and homophilies. Independently of its origin, this assortativity gives us information about each node given its neighbors. Assortativity can thus be used to improve individual predictions in a broad range of situations,...
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...
We present a novel use for self-organizing maps (SOMs) as an essential building block for incremental learning algorithms. SOMs are very well suited for this purpose because they are inherently online learning algorithms, because their weight updates are localized around the best-matching unit, which inherently protects them against catastrophic forgetting, and last but not least because they have...
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...
The advantage of prototype based learning vector quantizers are the intuitive and simple model adaptation as well as the easy interpretability of the prototypes as class representatives for the class distribution to be learned. Although they frequently yield competitive performance and show robust behavior nowadays powerful alternatives have increasing attraction. Particularly, deep architectures...
Multiple Sclerosis (MS) is the most prevalent demyelinating disease of the Central Nervous System, being the Relapsing-Remitting (RRMS) its most common subtype. We explored here the viability of use of Self Organizing Maps (SOM) to perform automatic segmentation of MS lesions apart from CNS normal tissue. SOM were able, in most cases, to successfully segment MRIs of patients with RRMS, with the correct...
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...
In this paper we evaluate metaheuristic optimization methods on a partitional clustering task of a real-world supply chain dataset, aiming at customer segmentation. For this purpose, we rely on the automatic clustering framework proposed by Das et al. [1], named henceforth DAK framework, by testing its performance for seven different metaheuristic optimization algorithm, namely: simulated annealing...
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