mlsanityData to 14 Oct 2026

Mask-informed Deep Contrastive Incomplete Multi-view Clustering

arXiv 2502.02234 ↗First posted 4 Feb 2025cs.CV cs.LGcited by 0

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Abstract (arXiv)

Multi-view clustering (MvC) utilizes information from multiple views to uncover the underlying structures of data. Despite significant advancements in MvC, mitigating the impact of missing samples in specific views on the integration of knowledge from different views remains a critical challenge. This paper proposes a novel Mask-informed Deep Contrastive Incomplete Multi-view Clustering (Mask-IMvC) method, which elegantly identifies a view-common representation for clustering. Specifically, we introduce a mask-informed fusion network that aggregates incomplete multi-view information while considering the observation status of samples across various views as a mask, thereby reducing the adverse effects of missing values. Additionally, we design a prior knowledge-assisted contrastive learning loss that boosts the representation capability of the aggregated view-common representation by injecting neighborhood information of samples from different views. Finally, extensive experiments are conducted to demonstrate the superiority of the proposed Mask-IMvC method over state-of-the-art approaches across multiple MvC datasets, both in complete and incomplete scenarios.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 74 references that are in the map. First posted on arXiv 4 Feb 2025.

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30 of 74 references.

References · 30

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18 more
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