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Efficient Multi-View K -Means for Image Clustering
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AI
DOI：10.1109/TIP.2023.3340609.png)
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摘要
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Nowadays, data in the real world often comes from multiple sources, but most existing multi-view K-Means perform poorly on linearly non-separable data and require initializing the cluster centers and calculating the mean, which causes the results to be unstable and sensitive to outliers. This paper proposes an efficient multi-view K -Means to solve the above-mentioned issues. Specifically, our model avoids the initialization and computation of clusters centroid of data. Additionally, our model use the Butterworth filters function to transform the adjacency matrix into a distance matrix, which makes the model is capable of handling linearly inseparable data and insensitive to outliers. To exploit the consistency and complementarity across multiple views, our model constructs a third tensor composed of discrete index matrices of different views and minimizes the tensor's rank by tensor Schatten p-norm. Experiments on two artificial datasets verify the superiority of our model on linearly inseparable data, and experiments on several benchmark datasets illustrate the performance.
Keyword:
Multi-view clustering
unsupervised learning
tensor low-rank constraint
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
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分享收藏Evaluation of the performance of clustering algorithms in kernel-induced feature space.png)
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