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Safe maximin multiple graph learning for multi-view clustering
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DOI：10.1007/s10489-026-07307-w.png)
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Abstract
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Graph-based multi-view clustering (MVC) has attracted considerable research attention due to its effectiveness in fusing multi-view information. Although many graph-based MVC methods have been proposed, the safety of this kind of method is rarely considered, limiting their application. In this paper, we propose a novel graph-based multi-view clustering method, named safe maximin multiple graph learning, to obtain the safety in graph learning. The proposed model introduces the maximizing performance gain strategy into the graph learning while performing the multiple graph fusion. Meanwhile, we develop a theorem to ensure the safety of the proposed model, such that it can obtain safe clustering result over the baseline clustering result. To solve the proposed maximin optimization problem, an effective algorithm is developed, together with a remark to analyze the safety of the proposed algorithm. Experimental results indicate that the proposed method can achieve safe clustering results, and obtain competitive multi-view clustering performance, compared to state-of-the-art approaches.
Keywords:
Multi-view clustering
Safe machine learning
Graph learning
Maximin optimization
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3.5
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7.6K
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1.7W
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APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
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