Afficher la couverture 'Accelerating stochastic kernel SOM | Mariette, Jérôme, J.' en grand format
/5

0 avis

Accelerating stochastic kernel SOM

Archive ouverte

Mariette, Jérôme, J. | Rossi, Fabrice | Olteanu, Madalina | Vialaneix, Nathalie

Edité par CCSD ; d-side publications -

International audience. Analyzing non vectorial data has become a common trend in a number of real-life applications. Various prototype-based methods have been extended to answer this need by means of kernalization that embed data into an (implicit) Euclidean space. One drawback of those approaches is their complexity, which is commonly of order the square or the cube of the number of observations. In this paper, we propose an efficient method to reduce complexity of the stochastic kernel SOM. The results are illustrated on large datasets and compared to the standard kernel SOM. The approach has been implemented in the last version of the R package SOMbrero.

Suggestions

Du même auteur

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...

Kernel and dissimilarity methods for exploratory analysis in a social context

Archive ouverte | Mariette, Jérôme, J. | CCSD

International audience. While most of statistical methods for prediction or data mining have been built for data made of independent observations of a common set of p numerical variables, many real-world application...

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 ...

Chargement des enrichissements...