One-Stage Fair Multi-View Spectral Clustering

Multi-view clustering is an important task in multimedia and machine learning. In multi-view clustering, multi-view spectral clustering is one kind of the most popular and effective methods. However, existing multi-view spectral clustering ignores the fairness in the clustering result, which may cause discrimination. To tackle this problem, in this paper, we propose an innovative Fair Multi-view Spectral Clustering (FMSC) method. Firstly, we provide a new perspective of fairness from the graph theory viewpoint, which constructs a relation between fairness and the average degree in graph theory. Secondly, based on this relation, we design a novel fairness-aware regularized term, which has the same form as the ratio cut in spectral clustering. Thirdly, we seamlessly plug this fairness-aware regularized term into the multi-view spectral clustering, leading to our one-stage FMSC, which can directly obtain the final clustering result without any post-processing. We also conduct extensive experiments compared with state-of-the-art fair clustering and multi-view clustering methods, which shows that our method can achieve better fairness.