A Unified Collaborative Multi-kernel Fuzzy Clustering for Multiview Data

Abstract

Clustering is increasingly important for multiview data analytics and current algorithms are either based on the collaborative learning of local partitions or directly derived global clustering from multikernel learning. In this paper, we innovate a clustering model that unifies the local partitions and global clustering in a collaborative learning framework. We first construct a common multikernel space from a set of basis kernels to better reflect clustering information of each individual view. Then, considering that joint local partitions would conform to the global clustering, we fuse the local partitions and global clustering guidance as a single objective function in accordance with fuzzy clustering form. The collaborative learning strategy enables the mutual and interactive clustering from local partitions and global clustering. The validation was performed over two synthetic and four public databases and the clustering accuracy was measured by normalized mutual information and rand index. The experimental results demonstrated that the proposed algorithm outperformed the related state-of-the-art algorithms in comparison, which included multitask, multikernel, and multiview clustering approaches.