Abstract

Collaborative clustering is an emerging field in machine learning that reveals the common structure within relevant and noisy datasets distributed across different sites. The value of collaborative clustering lies in its wide range of potential applications, including multi-view clustering and knowledge transfer. In this paper, a hybrid collaborative clustering approach is proposed that combines both horizontal and vertical collaborative clustering using AdSOM, which is efficient for all prototype-based clustering methods. The benefit of collaboration between datasets is quantified using a collaboration coefficient, which is evaluated iteratively during the collaboration step. This process is optimized using the steepest descent method. To demonstrate the effectiveness of the proposed collaborative approaches, a case study is conducted on phoneme recognition in continuous speech and in a speaker-independent context. The collaborative approaches are validated using two datasets: TIMIT and NTIMIT. Experimental results show promising performance.

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