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.

翻译：一致性与互补性是提升多视图聚类（MVC）性能的两个关键要素。近年来，随着对比学习的广泛应用，MVC中的视图一致性学习得到了进一步增强，并展现出优异的性能。然而相比之下，互补性学习尚未受到足够重视，现有方法通常仅从特征层面（例如采用希尔伯特-施密特独立性准则项或独立编码器-解码器网络）捕获视图特定信息。这促使我们在保持视图一致性的同时，从特征层面、视图标签层面和对比层面等多个维度重新全面审视视图的互补性学习。实证研究表明，所有层面都对互补性学习具有贡献，尤其是常被现有方法忽视的视图标签层面。基于此，我们自然发展出一个简单而有效的多视图聚类多层面互补性学习框架（MCMVC），该框架融合了多层面互补性信息，特别是显式嵌入了视图标签信息。据我们所知，这是首次显式利用视图标签指导视图互补性学习。与最先进的基线方法相比，MCMVC取得了显著提升，例如在Caltech101-20数据集上，针对完整与不完整MVC设置，三项评估指标的平均提升幅度分别超过$5.00\%$和$7.00\%$。