From Horizontal to Vertical Collaborative Clustering using Generative Topographic Maps
Résumé
Collaborative clustering is a recent field of Machine Learning that shows similarities
with both ensemble learning and transfer learning. Using a two-step approach
where different clustering algorithms first process data individually and
then exchange their information and results with a goal of mutual improvement,
collaborative clustering has shown promising performances when trying to have
several algorithms working on the same data. However the field is still lagging
behind when it comes to transfer learning where several algorithms are working
on different data with similar clusters and the same features.
In this article, we propose an original method where we combine the topological
structure of the Generative Topographic Mapping (GTM) algorithm and take
advantage of it to transfer information between collaborating algorithms working
on different data sets featuring similar distributions.
The proposed approach has been validated on several data sets, and the experimental
results have shown very promising performances.
Origine : Fichiers produits par l'(les) auteur(s)
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