Multi-view clustering has attracted widespread attention because of its capability to identify the common semantics shared by the data captured from different views of data, objects or phenomena. This is a challenging problem but with the emergence of deep auto-encoder networks, the performance of multi-view clustering methods has considerably improved. However, it is notable that most existing methods merely utilize the features outputted by the last encoder layer to carry out the clustering task. Such approach neglects potentially useful information conveyed by the features of the previous layers. To address the this problem, we propose a novel multi-layer multi-level comprehensive learning framework for deep multi-view clustering (3MC). 3MC firstly conducts a contrastive learning involving different views based on deep features in each encoder layer separately, so as to achieve multi-view feature consistency. The next step is to construct layer-specific label MLPs to transform the features in each layer to high-level semantic labels. Finally, 3MC conducts an inter-layer contrastive learning using the high-level semantic labels in order to obtain multi-layer consistent clustering assignments. We demonstrate that the proposed comprehensive learning strategy, commencing from layer specific inter-view feature comparison to inter-layer high-level label comparison extracts and utilizes the underlying multi-view complementary information very successfully and achieves more accurate clustering. An extensive experimental comparison with the state-of-the-art methods demonstrates the effectiveness of the proposed framework. The code of this paper is available at https://github.com/chenzhe207/3MC.

中文翻译：

---

多层次多层次综合学习，深度多视图聚类


多视图聚类引起了广泛关注，因为它能够识别从数据、对象或现象的不同视图中捕获的数据所共享的共同语义。这是一个具有挑战性的问题，但随着深度自编码器网络的出现，多视图聚类方法的性能得到了显著提高。然而，值得注意的是，大多数现有方法仅利用最后一个编码器层输出的特征来执行聚类任务。这种方法忽略了前几层的特征所传达的潜在有用信息。为了解决这个问题，我们提出了一种新的用于深度多视图聚类的多层多层次综合学习框架（3MC）。3MC 首先根据每个编码器层中的深度特征分别进行涉及不同视图的对比学习，从而实现多视图特征一致性。下一步是构建特定于层的标签 MLP，以将每层中的特征转换为高级语义标签。最后，3MC 使用高级语义标签进行层间对比学习，以获得多层一致的聚类分配。我们证明了所提出的综合学习策略，从特定层的视图间特征比较开始，到层间高级标签比较，非常成功地提取和利用了底层的多视图互补信息，并实现了更准确的聚类。与最先进方法的广泛实验比较证明了所提出的框架的有效性。本文的代码可在 https://github.com/chenzhe207/3MC 上获得。