Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Information
This work addresses multi-view clustering in unsupervised scenarios, offering an incremental improvement by incorporating diverse shallow features alongside deep consistency.
The paper tackles the problem of deep contrastive multi-view clustering by proposing CodingNet, which simultaneously explores diverse and consistent information across views, achieving superior performance on six benchmark datasets compared to most state-of-the-art methods.
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.