@inproceedings{
jiang2024heterogeneityaware,
title={Heterogeneity-Aware Federated Deep Multi-View Clustering towards Diverse Feature Representations},
author={Xiaorui Jiang and Zhongyi Ma and Yulin Fu and Yong Liao and Pengyuan Zhou},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=3fgY4qOhoO}
}
Multi-view clustering has garnered increasing attention in recent years because of its ability to extract consistent and complementary information from multi-view data. In this context, contrastive learning has often been employed to explore the common semantics across different views. However, we notice that existing multi-view contrastive clustering methods often overlook cases where samples belonging to the same cluster but different views are incorrectly classified as negative feature pairs, leading to larger separations between features belonging to the same cluster in the feature space. To address this issue, we propose to shift the perspective from the view-level to the cluster-level and introduce $\mathbf{C}$luster-level $\mathbf{C}$ontrastive $\mathbf{D}$eep $\mathbf{M}$ulti-$\mathbf{V}$iew $\mathbf{C}$lustering ($\mathbf{CCDMVC}$) method based on an intra-cluster negative pair exemption strategy. Specifically, by constructing global features to utilize complete view information, we infer the clustering probability of each sample, thus reducing the construction of negative feature pairs belonging to the same cluster. As a result, the contrastive loss is corrected, allowing the model to treat different levels of feature pairs differently, minimizing the introduction of noise and making the sample points within the same cluster more compact. Additionally, we propose a cluster-level imputation module to make CCDMVC compatible with scenarios involving incomplete data. This module infers missing features with high confidence clustering probabilities and classifies them in cluster-level form. We conduct extensive experiments on eight datasets with fourteen baseline algorithms. The results demonstrate that CCDMVC exhibits superior clustering performance.
Heterogeneity-Aware Federated Deep Multi-View Clustering towards Diverse Feature Representations
Xiaorui Jiang Zhongyi Ma Yulin Fu Yong Liao Pengyuan Zhou