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As the result of the decoding of large numbers of genome sequences, numerous proteins whose functions cannot be identified by the homology search of amino acid sequences have accumulated and remain of no use to science and industry. Establishment of novel prediction methods for protein function is urgently needed. We previously developed Batch-Learning SOM (BL-SOM) for genome informatics; here, we...
We propose Merge Growing Neural Gas (MGNG) as a novel unsupervised growing neural network for time series analysis. MGNG combines the state-of-the-art recursive temporal context of Merge Neural Gas (MNG) with the incremental Growing Neural Gas (GNG) and enables thereby the analysis of unbounded and possibly infinite time series in an online manner. There is no need to define the number of neurons...
The application of Self-Organizing Map (SOM) to hierarchical data remains an open issue, because such data lack inherent quantitative information. Past studies have suggested binary encoding and Generalizing SOM as techniques that transform hierarchical data into numerical attributes. Based on graph theory, this paper puts forward a novel approach that processes hierarchical data into a numerical...
In this paper we evaluate competitive learning algorithms in the task of identifying anomalous patterns in time series data. The methodology consists in computing decision thresholds from the distribution of quantization errors produced by normal training data. These thresholds are then used for classifying incoming data samples as normal/abnormal. For this purpose, we carry out performance comparisons...
Aircraft engines are designed to be used during several tens of years. Their maintenance is a challenging and costly task, for obvious security reasons. The goal is to ensure a proper operation of the engines, in all conditions, with a zero probability of failure, while taking into account aging. The fact that the same engine is sometimes used on several aircrafts has to be taken into account too...
Vector quantization methods are confronted with a model selection problem, namely the number of prototypical feature representatives to model each class. In this paper we present an incremental learning scheme in the context of figure-ground segmentation. In presence of local adaptive metrics and supervised noisy information we use a parallel evaluation scheme combined with a local utility function...
We have proposed Supervised Pareto Learning Self Organizing Maps(SP-SOM) based on the concept of Pareto optimality for the integration of multiple vectors and applied SP-SOM to the biometric authentication system which uses multiple behavior characteristics as feature vectors. In this paper, we examine performance of SP-SOM for the generic classification problem using iris data set. Furthermore, we...
In this paper, we introduce the Gamma SOM model for temporal sequence processing. The standard SOM is merged with a new context descriptor based on a short term memory structure called Gamma memory. The proposed model allows increasing depth without losing resolution, by adding more contexts. When using a single stage of the Gamma filter, the Merge SOM model is recovered. The temporal quantization...
In this paper we extend the crisp Affinity Propagation (AP) cluster algorithm to a fuzzy variant. AP is a message passing algorithm based on the max-sum-algorithm optimization for factor graphs. Thus it is applicable also for data sets with only dissimilarities known, which may be asymmetric. The proposed Fuzzy Affinity Propagation algorithm (FAP) returns fuzzy assignments to the cluster prototypes...
Swarm-based methods are promising nature-inspired techniques. A swarm of stochastic agents performs the task of clustering high-dimensional data on a low-dimensional output space. Most swarm methods are derivatives of the Ant Colony Clustering (ACC) approach proposed by Lumer and Faieta. Compared to clustering on Emergent Self-Organizing Maps (ESOM) these methods usually perform poorly in terms of...
This paper presents a simple way to compensate the magnification effect of Self-Organizing Maps (SOM) when creating cartograms using Carto-SOM. It starts with a brief explanation of what a cartogram is, how it can be used, and what sort of metrics can be used to assess its quality. The methodology for creating a cartogram with a SOM is then presented together with an explanation of how the magnification...
In this paper, we discuss problems related to the basic Semantic Web methodologies that are based on predicate logic and related formalisms. We discuss complementary and alternative approaches. In particular, we suggest how the Self-Organizing Map can be a basis for making the Semantic Web more semantic.
The self-organizing map (SOM) is a classical neural network method for dimensionality reduction and data visualization. Visualization induced SOM (ViSOM) and growing ViSOM (gViSOM) are two recently proposed variants for a more faithful, metric-based and direct data representation. They learn local quantitative distances of data by regularizing the inter-neuron contraction force while capturing the...
We propose a new gesture recognition method which is called “early recognition”. Early recognition is a method to recognize sequential patterns at their beginning parts. Therefore, in the case of gesture recognition, we can get a recognition result of human gestures before the gestures have finished. We realize early recognition by using sparse codes of Self-Organizing Map.
Bag of features is a well established technique for the visual categorisation of objects, categories of objects and textures. One of the most important part of this technique is codebook generation since its within-class and between-class discrimination power is the main factor in the categorisation accuracy. A codebook is generated from regions of interest extracted automatically from a set of labeled...
The self-organizing map (SOM) is related to the classical vector quantization (VQ). Like in the VQ, the SOM represents a distribution of input data vectors using a finite set of models. In both methods, the quantization error (QE) of an input vector can be expressed, e.g., as the Euclidean norm of the difference of the input vector and the best-matching model. Since the models are usually optimized...
We show how the “Online Sparse Coding Neural Gas” algorithm can be applied to a more realistic model of the “Cocktail Party Problem”. We consider a setting where more sources than observations are given and additive noise is present. Furthermore, we make the model even more realistic, by allowing the mixing matrix to change slowly over time. We also process the data in an online pattern-by-pattern...
This paper is devoted to the analysis of career paths and employability. The state-of-the-art on this topic is rather poor in methodologies. Some authors propose distances well adapted to the data, but are limiting their analysis to hierarchical clustering. Other authors apply sophisticated methods, but only after paying the price of transforming the categorical data into continuous, via a factorial...
This study proposes Network-Structured Particle Swarm Optimizer (NS-PSO) with various neighborhood topology. The proposed PSO has the various network topology as rectangular, hexagonal, cylinder and toroidal. We apply NS-PSO with various topology to optimization problems. We investigate their behaviors and evaluate what kind of topology would be the most appropriate for each function.
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