Multi-View Bipartite Graph Clustering With Coupled Noisy Feature Filter

Liang Li, Junpu Zhang, Siwei Wang, Xinwang Liu, Kenli Li, Keqin Li

Abstract:

Unsupervised bipartite graph learning has been a hot
topic in multi-view clustering, to tackle the restricted scalability
issue of traditional full graph clustering in large-scale applications.
However, the existing bipartite graph clustering paradigm pays
little attention to the adverse impact of noisy features on learning
process. To further facilitate this part of research, apart from simply
reweighting features to depress the noisy ones, we take the first
step towards analyzing the induced adverse impact via theoretical
and experimental investigations. One crucial finding in this article
is that the existence of noisy features will incur “anchor shift”
phenomenon, which deviates from the potential representations of
anchors and then degrades performance. To this end, we propose a
coupled noisy feature filter mechanism with automatically finding
feature importance to remedy the anchor shift issue in this article.
Apart fromleveraging features,we theoretically analyze the bounds
of proposed feature-adaptive bipartite graph’s fuzzy membership.
Specifically, distinguishing features’ discrimination will increase
the fuzzy membership to achieve soft partitions against the potential
inaccurate absolute relationships. With the afore-mentioned
merits, our proposed multi-view bipartite graph clustering with
coupled noisy feature filter model (MVBGC-NFF) provides novel
and interesting insights on the feature level of anchor shift. The
effectiveness and efficiency of MVBGC-NFF are demonstrated on
synthetic and real-world datasets with improved clustering performance,
increasing fuzzy membership, and filtering noisy features.
The code is available on https://github.com/liliangnudt/MVBGCNFF.