Multi-View Clustering with Granularity-Aware Pseudo Supervision

Jie Yang, Cheng-You Lu, Zhongli Wang, Hsiang-Ting Chen, Guang-Kui Xu, Chenglong Zhang, Shuting Dong, Xinyan Liang, Bingbing Jiang

Abstract:

Modern multi-view clustering (MVC) is dominated by two
paradigms: multi-view fusion and pseudo-label-guided learn
ing. Pseudo-labeling methods can suffer from confirmation
bias; their reliance on a fixed-granularity supervision from an
initial clustering can cause learned embeddings to drift from
the data’s true structure and lose discriminative power. Con
versely, fusion methods excel at integrating information but
often struggle to robustly differentiate between high-quality
and noisy views, which can obscure final cluster boundaries
and degrade performance. To address these complementary
challenges, we propose GAPS (Granularity-Aware Pseudo
Supervision), a novel MVC framework. GAPS introduces
a granularity-aware supervision mechanism that generates a
full hierarchy of pseudo-labels, enabling the selection of a
supervision level that best aligns with the data’s intrinsic
multi-scale structure. Furthermore, to ensure a high-quality
supervisory signal, it incorporates a reliability-aware view
selection strategy using a novel Separation-Compactness In
dex (SCI) to identify and leverage the most informative view
for pseudo-label generation. This dual approach ensures the
supervisory signal is both structurally adaptive and derived
from the most reliable source, leading to highly effective fi
nal representations. Extensive experiments on synthetic and
real-world datasets demonstrate the effectiveness and superi
ority of GAPS over other competitors.