@inproceedings{
zhao2024dfmvc,
title={{DFMVC}: Deep Fair Multi-view Clustering},
author={Bowen Zhao and Qianqian Wang and ZHIQIANG TAO and Wei Feng and Quanxue Gao},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=IgQ3xrYXTc}
}
Existing fair multi-view clustering methods impose a constraint that requires the distribution of sensitive attributes to be uniform within each cluster. However, this constraint can lead to misallocation of samples with sensitive attributes. To solve this problem, we propose a novel Deep Fair Multi-View Clustering (DFMVC) method that learns a consistent and discriminative representation instructed by a fairness constraint constructed from the distribution of clusters. Specifically, we incorporate contrastive constraints on semantic features from different views to obtain consistent and discriminative representations for each view. Additionally, we align the distribution of sensitive attributes with the target cluster distribution to achieve optimal fairness in clustering results. Experimental results on four datasets with sensitive attributes demonstrate that our method improves both the fairness and performance of clustering compared to state-of-the-art multi-view clustering methods.
DFMVC: Deep Fair Multi-view Clustering
Bowen Zhao Qianqian Wang ZHIQIANG TAO Wei Feng Quanxue Gao