Multiple-view clustering for correlation matrices based on Wishart mixture model

by   Tomoki Tokuda, et al.

A multiple-view clustering method is a powerful analytical tool for high-dimensional data, such as functional magnetic resonance imaging (fMRI). It can identify clustering patterns of subjects depending on their functional connectivity in specific brain areas. However, when one applies an existing method to fMRI data, there is a need to simplify the data structure, independently dealing with elements in a functional connectivity matrix, that is, a correlation matrix. In general, elements in a correlation matrix are closely associated. Hence, such a simplification may distort the clustering results. To overcome this problem, we propose a novel multiple-view clustering method based on the Wishart mixture model, which preserves the correlation matrix structure. The uniqueness of this method is that the multiple-view clustering of subjects is based on particular networks of nodes (or regions of interest (ROIs) in fMRI), optimized in a data-driven manner. Hence, it can identify multiple underlying pairs of associations between a subject cluster solution and a ROI network. The key assumption of the method is independence among networks, which is effectively addressed by whitening correlation matrices. We applied the proposed method to synthetic and fMRI data, demonstrating the usefulness and power of the proposed method.


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