论文标题
显式视图标签重要：多视图集群的多方体互补性研究
Explicit View-labels Matter: A Multifacet Complementarity Study of Multi-view Clustering
论文作者
论文摘要
一致性和互补性是增强多视图聚类（MVC）的两种关键要素。最近，随着流行的对比学习的引入，MVC的观点一致性学习进一步增强，从而导致了有希望的表现。但是，相比之下，互补性尚未得到足够的关注，除了在功能方面，希尔伯特·施密特（Hilbert Schmidt）独立标准项或独立的编码器网络通常被采用以捕获特定视图的信息。这促使我们从包括功能，视图标签和对比方面在内的多个方面全面地重新考虑互补性学习观点，同时保持视图一致性。我们从经验上发现，所有方面都有助于互补学习，尤其是视图标签的方面，通常被现有方法忽略了。基于此，一个简单而有效的\下划线{m} ultifacet \ useverline {c} omplementarity学习框架\ suespline {m} upsi- \ usepline {uspline {v} iew \ lissionline {c} lustering {c} lustering（mcmvc）自然开发，构成型互补信息。据我们所知，这是第一次明确使用视图标签来指导视图的互补性学习。与SOTA基准相比，MCMVC在$ 5.00 \％$ $ $ $ $ 5.00 \％$和$ 7.00 \％$中的平均利润分别在CALTECH101-20上分别以三个评估指标的方式实现了显着的改进。
Consistency and complementarity are two key ingredients for boosting multi-view clustering (MVC). Recently with the introduction of popular contrastive learning, the consistency learning of views has been further enhanced in MVC, leading to promising performance. However, by contrast, the complementarity has not received sufficient attention except just in the feature facet, where the Hilbert Schmidt Independence Criterion term or the independent encoder-decoder network is usually adopted to capture view-specific information. This motivates us to reconsider the complementarity learning of views comprehensively from multiple facets including the feature-, view-label- and contrast- facets, while maintaining the view consistency. We empirically find that all the facets contribute to the complementarity learning, especially the view-label facet, which is usually neglected by existing methods. Based on this, a simple yet effective \underline{M}ultifacet \underline{C}omplementarity learning framework for \underline{M}ulti-\underline{V}iew \underline{C}lustering (MCMVC) is naturally developed, which fuses multifacet complementarity information, especially explicitly embedding the view-label information. To our best knowledge, it is the first time to use view-labels explicitly to guide the complementarity learning of views. Compared with the SOTA baselines, MCMVC achieves remarkable improvements, e.g., by average margins over $5.00\%$ and $7.00\%$ respectively in complete and incomplete MVC settings on Caltech101-20 in terms of three evaluation metrics.
