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Fine-grained tensor completion for incomplete multi-view clustering
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DOI：10.1016/j.patcog.2025.112956.png)
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摘要
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Incomplete multi-view clustering (IMC) is to cluster data from multiple views, where some views may contain missing entries. In this paper, we propose a novel method for IMC. First, we construct cross-order neighbor graphs of the observed entries in different views, which preserve comprehensive and complementary information of the data. Then, we recover a fine-grained low-rank tensor of the incomplete data from the incomplete cross-order neighbor graphs to preserve cross-view consistency using the low-rank tensor completion technique, where a novel log-based tensor approximation is developed for rank constraint. Moreover, we incorporate clustering-driven structural constraints with auto-adjusted weights to preserve cross-view diversity, which promotes the recovered low-rank tensor to be more suitable for clustering applications. Extensive experimental results confirm the superiority of the proposed method compared with state-of-the-art IMC methods.
Keyword:
Incomplete multi-view clustering
Cross-order neighbor
Tensor completion
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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分享收藏Nonconvex low-rank tensor approximation with graph and consistent regularizations for multi-view subspace learning.png)
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