%0 Conference Paper %F Oral %T Vertical collaborative clustering using generative topographic maps %+ Mathématiques et Informatique Appliquées (MIA-Paris) %+ Laboratoire d'Informatique de Paris-Nord (LIPN) %A Sublime, Jérémie %A Grozavu, Nistor %A Bennani, Younes %A Cornuéjols, Antoine %< avec comité de lecture %@ 978-1-4673-9360-7 %3 IEEE Explore %B 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR) %C Fukuoka, Japan %I IEEE %C New York (united states) %S 2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR) %P 199-204 %8 2015-11-13 %D 2015 %R 10.1109/SOCPAR.2015.7492807 %Z Statistics [stat]Conference papers %X Collaborative clustering is a recent field of Machine Learning that shows similarities with both transfer learning and ensemble learning. It uses two-step approaches where different clustering algorithms first process data individually and then exchange their information and results with a goal of mutual improvement. In this article, we introduce a new collaborative learning approach based on collaborative clustering principles and applied to the Generative Topographic Mapping (GTM) algorithm. Our method consists in applying the GTM algorithm on different data sets where similar clusters can be found (same feature spaces and similar data distributions), and then to use a collaborative framework on the generated maps with the goal of transferring knowledge between them. The proposed approach has been validated on several data sets, and the experimental results have shown very promising performances. %G English %L hal-01589449 %U https://agroparistech.hal.science/hal-01589449 %~ UNIV-PARIS13 %~ AGROPARISTECH %~ CNRS %~ INRA %~ LIPN %~ MIA-PARIS %~ USPC %~ UNIV-PARIS-SACLAY %~ AGREENIUM %~ AGROPARISTECH-SACLAY %~ INRA-SACLAY %~ AGROPARISTECH-MMIP %~ AGROPARISTECH-ORG %~ GALILE %~ APT_SP %~ INRAE %~ SORBONNE-PARIS-NORD %~ GS-COMPUTER-SCIENCE %~ MATHNUM