非对称双翼多视图聚类网络用于探索多样性与一致性信息
Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Information
AI总结：
提出非对称双翼网络CodingNet，同时挖掘多视图数据的浅层多样性与深层一致性信息，通过双重对比机制提升聚类性能，在六个基准数据集上超越现有方法。
摘要：
在无监督场景下，深度对比多视图聚类（DCMVC）正成为研究热点，其旨在挖掘不同视图之间的潜在关系。现有大多数DCMVC算法专注于探索深度语义特征的一致性信息，而忽略了浅层特征上的多样性信息。为填补这一空白，本文提出一种新颖的多视图聚类网络，称为CodingNet，以同时探索多样性与一致性信息。具体而言，我们摒弃传统的自编码器，设计了一种非对称结构网络来分别提取浅层和深层特征。然后，通过将浅层特征上的相似性矩阵对齐到零矩阵，确保浅层特征的多样性，从而更好地描述多视图数据。此外，我们提出一种双重对比机制，在视图特征和伪标签两个层面上维持深层特征的一致性。通过在六个广泛使用的基准数据集上进行大量实验，验证了该框架的有效性，其性能优于大多数最先进的多视图聚类算法。
英文摘要：
In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is becoming a hot research spot, which aims to mine the potential relationships between different views. Most existing DCMVC algorithms focus on exploring the consistency information for the deep semantic features, while ignoring the diverse information on shallow features. To fill this gap, we propose a novel multi-view clustering network termed CodingNet to explore the diverse and consistent information simultaneously in this paper. Specifically, instead of utilizing the conventional auto-encoder, we design an asymmetric structure network to extract shallow and deep features separately. Then, by aligning the similarity matrix on the shallow feature to the zero matrix, we ensure the diversity for the shallow features, thus offering a better description of multi-view data. Moreover, we propose a dual contrastive mechanism that maintains consistency for deep features at both view-feature and pseudo-label levels. Our framework's efficacy is validated through extensive experiments on six widely used benchmark datasets, outperforming most state-of-the-art multi-view clustering algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
