Adversarial Fair Incomplete Multi-View Clustering

Qianqian Wang; Haiming Xu; Wei Feng; Quanxue Gao

AAAI 2026technical0 citations

Adversarial Fair Incomplete Multi-View Clustering

Qianqian Wang, Haiming Xu, Wei Feng, Quanxue Gao

Abstract

Fair incomplete multi-view clustering (FIMVC) confronts a critical yet unresolved challenge, as existing methods often fail to address the intertwined issues of data missingness and algorithmic bias simultaneously. In this paper, we propose a novel FIMVC method named Adversarial Fair Incomplete Multi-View Clustering (AFIMVC). The core of AFIMVC is a new adaptive adversarial disentanglement mechanism. This mechanism trains the feature encoder to produce representations that are invariant to sensitive attributes by adversary learning, where the adversarial intensity is dynamically controlled by the model

BibTeX

@inproceedings{aaai2026_adversarialfairi,
  title = {Adversarial Fair Incomplete Multi-View Clustering},
  author = {Qianqian Wang and Haiming Xu and Wei Feng and Quanxue Gao},
  booktitle = {AAAI 2026},
  year = {2026}
}