Abstract
Multi-view clustering is an active direction in machine learning and pattern recognition which aims at exploring the consensus and complementary information among multiple views. In the last few years, a number of methods based on multi-view learning have been widely investigated and achieved promising performance. Generally, classical multi-view clustering methods such as multi-view kernel k-means clustering are point-based methods. The performance of point-based methods will be fairly good when the data points are distributed around the center point. The plane-based clustering methods can handle data points that are clustered along a straight line and have never been investigated in multi-view learning. In this paper, we propose a novel multi-view k-proximal plane clustering method, which initializes cluster labels by multi-view spectralclustering and updates whole multi-view cluster hyperplanes and labels alternately until some stopping conditions are satisfied. Extensive experimental results on several benchmark datasets show that the proposed model outperforms other state-of-the-art multi-view algorithms.
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Acknowledgements
This work is supported by Ningbo University talent project 421703670 as well as programs sponsored by K.C. Wong Magna Fund in Ningbo University. It is also supported by NSFC 61906101 and 62006131.
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This article belongs to the Topical Collection: Special Issue on Multi-view Learning Guest Editors: Guoqing Chao, Xingquan Zhu, Weiping Ding, Jinbo Bi and Shiliang Sun
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Sun, F., Xie, X., Qian, J. et al. Multi-view k-proximal plane clustering. Appl Intell 52, 14949–14963 (2022). https://doi.org/10.1007/s10489-022-03176-1
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DOI: https://doi.org/10.1007/s10489-022-03176-1