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Multi-view k-proximal plane clustering
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DOI：10.1007/s10489-022-03176-1.png)
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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.
Keyword:
Multi-view clustering
K-proximal plane clustering
Plane-based clustering
期刊
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3.5
论文数:
7.6K
被引数:
1.7W
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