A supervised growing neural gas algorithm for cluster analysis

Jirayusakul, A.; Auwatanamongkol, Surapong

doi:10.3233/his-2007-4402

Cited by 7 publications

(8 citation statements)

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“…In order to overcome these problems, alternative supervised versions of Neural Gas (NG) [42] and Growing Neural Gas (GNG) [16] have been developed. These extensions have been called Supervised Neural Gas (SNG) [21] and Supervised Growing Neural Gas (SGNG) [31].…”

Section: Lvq Methods Based On Margin Maximizationmentioning

confidence: 99%

A review of learning vector quantization classifiers

Nova

Estévez

2013

Neural Comput & Applic

114

117

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Section: Lvq Methods Based On Margin Maximizationmentioning

confidence: 99%

A review of learning vector quantization classifiers

Nova

Estévez

2013

Neural Comput & Applic

114

117

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“…The Supervised Growing Neural Gas (SGNG) algorithm is a modification of the GNG algorithm that uses class labels of data to guide the partitioning of data into optimal clusters [20], [21]. Each of the initial neurons is labelled with a unique class label.…”

Section: Supervised Growing Neural Gasmentioning

confidence: 99%

“…where sn is the nearest class neuron and repulsion() is a function specifically introduced to maintain neurons sufficiently distant one each other. For the neuron that is topologically close to the neuron sn, the rule intends to increase the clustering accuracy [21]. The insertion mechanism has to reduce not only the intra-distances between data in a cluster, but also the impurity of the cluster.…”

Section: Supervised Growing Neural Gasmentioning

confidence: 99%

Neural Gas Based Classification of Globular Clusters

Angora

Brescia

Cavuoti

et al. 2018

Communications in Computer and Information Science

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0000−0002−0316−6562] , Massimo Brescia 2[0000−0001−9506−5680] , Stefano Cavuoti 1,2,3[0000−0002−3787−4196] , Giuseppe Riccio 2[0000−0001−7020−1172] , Maurizio Paolillo 1[0000−0003−4210−7693] , and Thomas H. Puzia 4[0000−0003−0350−7061]Abstract. Within scientific and real life problems, classification is a typical case of extremely complex tasks in data-driven scenarios, especially if approached with traditional techniques. Machine Learning supervised and unsupervised paradigms, providing self-adaptive and semi-automatic methods, are able to navigate into large volumes of data characterized by a multidimensional parameter space, thus representing an ideal method to disentangle classes of objects in a reliable and efficient way. In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band images, is one of such cases where self-adaptive methods demonstrated a high performance and reliability. Here we experimented some variants of the known Neural Gas model, exploring both supervised and unsupervised paradigms of Machine Learning for the classification of Globular Clusters. Main scope of this work was to verify the possibility to improve the computational efficiency of the methods to solve complex data-driven problems, by exploiting the parallel programming with GPU framework. By using the astrophysical playground, the goal was to scientifically validate such kind of models for further applications extended to other contexts.

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“…A widely used possibility is a global quality assessment. This information can be used to select the best performing set of prototypes after the network grew until a predefined maximum number of prototypes was reached [9], or to stop if the change in a quality measure does not significantly vary by adding further prototypes. An online scenario as well as noisy supervised information, which corrupt global quality assessments, prohibits such methods.…”

Section: Incremental Online Processingmentioning

confidence: 99%

Incremental Figure-Ground Segmentation Using Localized Adaptive Metrics in LVQ

Denecke

Wersing

Steil

et al. 2009

Advances in Self-Organizing Maps

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Abstract. 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 to organize a learning vector quantization (LVQ) network with an adaptive number of prototypes and verify the capabilities on a real world figure-ground segmentation task.

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A supervised growing neural gas algorithm for cluster analysis

Cited by 7 publications

References 0 publications

A review of learning vector quantization classifiers

A review of learning vector quantization classifiers

Neural Gas Based Classification of Globular Clusters

Incremental Figure-Ground Segmentation Using Localized Adaptive Metrics in LVQ

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