Multi-view Clustering via Multi-granularity Ensemble

Jie Yang, Wei Chen, Feng Liu, Peng Zhou, Zhongli Wang, Xinyan Liang, Bingbing Jiang

Abstract:

Multi-view clustering aims to integrate complementary
information from multiple views to improve
clustering performance. However, existing
ensemble-based methods suffer from information
loss due to their reliance on single-granularity
labels, limiting the discriminative capability of
learned representations. Meanwhile, representation
and graph fusion-based approaches face challenges
such as explicit view alignment and manual
weight tuning, making them less effective for
heterogeneous views with varying data distributions.
To address these limitations, we propose a
novel multi-view clustering framework via Multigranularity
Ensemble (MGE), fully using the multigranularity
information across diverse views for accurate
and consistent clustering. Specifically, MGE
first modifies the hierarchical clustering and then
leverages it on each view (including the fused view)
to achieve multi-granularity labels. Moreover, the
cross-view and cross-granularity fusion strategy is
designed to learn a robust co-association similarity
matrix, which effectively preserves the fine-grained
and coarse-grained structures of multi-view data
and facilitates subsequent clustering. Therefore,
MGE can provide a comprehensive representation
of local and global patterns within data, eliminating
the requirement for view alignment and weight
tuning. Experiments demonstrate that MGE consistently
outperforms state-of-the-art methods across
multiple datasets, validating its effectiveness and
superiority in handling heterogeneous views.