Afficher la couverture 'Aggregating Self-Organizing Maps with Topology Preservation | Mariette, Jérôme, J.' en grand format
/5

0 avis

Aggregating Self-Organizing Maps with Topology Preservation

Archive ouverte

Mariette, Jérôme, J. | Vialaneix, Nathalie

Edité par CCSD ; Springer International Publishing Switzerland -

International audience. In the online version of Self-Organizing Maps, the results obtained from different instances of the algorithm can be rather different. In this paper, we explore a novel approach which aggregates several results of the SOM algorithm to increase their quality and reduce the variability of the results. This approach uses the variability of the algorithm that is due to different initialization states. We use simulations to show that our result is efficient to improve the performance of a single SOM algorithm and to decrease the variability of the final solution. Comparison with existing methods for bagging SOMs also show competitive results.

Suggestions

Du même auteur

Unsupervised multiple kernel learning for heterogeneous data integration

Archive ouverte | Mariette, Jérôme, J. | CCSD

International audience. Motivation: Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has ...

Des noyaux pour les omiques

Archive ouverte | Mariette, Jérôme, J. | CCSD

International audience. Le développement des techniques de séquençage haut débit génère un volume de données en forte croissance à des coûts relativement faibles. Ces données sont souvent de très grande dimension, h...

Efficient interpretable variants of online SOM for large dissimilarity data

Archive ouverte | Mariette, Jérôme, J. | CCSD

International audience. Self-organizing maps (SOM) are a useful tool for exploring data. In its original version, the SOM algorithm was designed for numerical vectors. Since then, several extensions have been propos...

Chargement des enrichissements...