Optimizing exchange confidence during collaborative clustering
Jérémie Sublime, Denis Maurel, Nistor Grozavu, Basarab Mateï, Younès Bennani · 2018
Collaborative clustering is a recent learning paradigm concerned with the unsupervised analysis of complex multi-view data using several algorithms working together. Well known applications of collaborative clustering include multi-view clustering and distributed data clustering, where several algorithms exchange information in order to mutually improve each others based on the diversity of their models or their view of the data. However, many of the proposed algorithms and statistical models in these fields lack the capability to properly detect noisy views and sub-optimal collaborators. As a result, these weak collaborators and noisy views often go undetected during the collaborative process, and end up deteriorating the results of all other algorithms. In this article, we propose a weighting optimization method for the collaborative version of the SOM algorithm that will help detect whether local self-organizing maps should or should not exchange their information based on the diversity between their topologies. This method can further be used to detect noisy views and discard them in unsupervised collaborative and multi-view processes.