Multiple Partitions Aligned Clustering

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Authors Zipeng Guo, Shudong Huang, Yuanzhang Su, Wenyu Chen, Zhao Kang, Siying Wang, Zenglin Xu
Journal/Conference Name IJCAI International Joint Conference on Artificial Intelligence
Paper Category
Paper Abstract Multi-view clustering is an important yet challenging task due to the difficulty of integrating the information from multiple representations. Most existing multi-view clustering methods explore the heterogeneous information in the space where the data points lie. Such common practice may cause significant information loss because of unavoidable noise or inconsistency among views. Since different views admit the same cluster structure, the natural space should be all partitions. Orthogonal to existing techniques, in this paper, we propose to leverage the multi-view information by fusing partitions. Specifically, we align each partition to form a consensus cluster indicator matrix through a distinct rotation matrix. Moreover, a weight is assigned for each view to account for the clustering capacity differences of views. Finally, the basic partitions, weights, and consensus clustering are jointly learned in a unified framework. We demonstrate the effectiveness of our approach on several real datasets, where significant improvement is found over other state-of-the-art multi-view clustering methods.
Date of publication 2019
Code Programming Language MATLAB
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