Multi-View Transfer Clustering Algorithm Based on Gaussian Mixture Model

It is well known that the Gaussian mixture model (GMM) is an exemplary model-based clustering approach that captures data distribution patterns to facilitate clustering. However, when it faces to multi-view clustering tasks, the classical GMM algorithm often encounters limitations in scalability. Multi-view clustering entails employing diverse views or feature sets to cluster data, thereby achieving comprehensive and accurate clustering outcomes. The classical GMM algorithm fails to fully exploit the correlated information across multiple views, resulting in a decrease in clustering accuracy. This paper proposes a new GMM-based trans-fer multi-view clustering algorithm named MT-GMM. This algorithm transfers valuable knowledge from other views to guide and enhance the clustering task of the current view, thereby improving the clustering performance specifically for the current view. Experimental results conducted on real-world datasets demonstrate that the proposed MT-GMM outperforms several other multi-view clustering methods, as it can effectively harness the complementary information between different views accurately capture the structures of data.