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View-unaligned clustering with graph regularization
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DOI：10.1016/j.patcog.2024.110706.png)
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摘要
En 中文
In current multi -view clustering modeling scenarios, the cross -view correspondence of the data is generally presumed in advance. However, this assumption is inevitably violated in practical applications as each view is independently processed during data collection and transmission, thus resulting in the view -unaligned problem (VuP). The absence of cross -view correspondence between the data renders most existing multi -view clustering methods ineffective in addressing the VuP. To address this problem, we propose a novel view -unaligned clustering method, termed View -unaligned Clustering with Graph regularization (VuCG), which performs latent embedding learning on manifold, latent embedding alignment and partition generation in a one -stage manner. Specifically, we implement the latent embedding learning on manifold by seeking a matrix factorization respecting the graph structure, and perform latent embedding alignment by identifying the counterpart from a selected template of the shuffled embedding using global and local information of the data in the latent space. Additionally, we obtain the partition of the aligned data by applying the relaxed k -means algorithm to the aligned embeddings. Extensive experimental results on four practical datasets demonstrate the effectiveness of the proposed method.
Keyword:
Multi-view clustering
View-unaligned problem
Latent embedding learning
Latent embedding alignment
Matrix factorization
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
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