Adversarial Fair Multi-View Clustering

TL;DR

This paper introduces an adversarial framework for multi-view clustering that enhances fairness by removing sensitive attribute influence from learned features, ensuring fair and consistent clustering without sacrificing performance.

Contribution

It proposes a novel adversarial training approach for fair multi-view clustering that theoretically guarantees fairness and clustering consistency.

Findings

Achieves superior fairness compared to existing methods.

Maintains competitive clustering performance.

Provides theoretical guarantees for fairness and consistency.

Abstract

Cluster analysis is a fundamental problem in data mining and machine learning. In recent years, multi-view clustering has attracted increasing attention due to its ability to integrate complementary information from multiple views. However, existing methods primarily focus on clustering performance, while fairness-a critical concern in human-centered applications-has been largely overlooked. Although recent studies have explored group fairness in multi-view clustering, most methods impose explicit regularization on cluster assignments, relying on the alignment between sensitive attributes and the underlying cluster structure. However, this assumption often fails in practice and can degrade clustering performance. In this paper, we propose an adversarial fair multi-view clustering (AFMVC) framework that integrates fairness learning into the representation learning process. Specifically, our method employs adversarial training to fundamentally remove sensitive attribute information from learned features, ensuring that the resulting cluster assignments are unaffected by it. Furthermore, we theoretically prove that aligning view-specific clustering assignments with a fairness-invariant consensus distribution via KL divergence preserves clustering consistency without significantly compromising fairness, thereby providing additional theoretical guarantees for our framework. Extensive experiments on data sets with fairness constraints demonstrate that AFMVC achieves superior fairness and competitive clustering performance compared to existing multi-view clustering and fairness-aware clustering methods.