Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and
Prototype Alignment
- URL: http://arxiv.org/abs/2303.15689v2
- Date: Thu, 30 Mar 2023 13:53:11 GMT
- Title: Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and
Prototype Alignment
- Authors: Jiaqi Jin, Siwei Wang, Zhibin Dong, Xinwang Liu, En Zhu
- Abstract summary: We propose a Cross-view Partial Sample and Prototype Alignment Network (CPSPAN) for Deep Incomplete Multi-view Clustering.
Unlike existing contrastive-based methods, we adopt pair-observed data alignment as 'proxy supervised signals' to guide instance-to-instance correspondence construction.
- Score: 50.82982601256481
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The success of existing multi-view clustering relies on the assumption of
sample integrity across multiple views. However, in real-world scenarios,
samples of multi-view are partially available due to data corruption or sensor
failure, which leads to incomplete multi-view clustering study (IMVC). Although
several attempts have been proposed to address IMVC, they suffer from the
following drawbacks: i) Existing methods mainly adopt cross-view contrastive
learning forcing the representations of each sample across views to be exactly
the same, which might ignore view discrepancy and flexibility in
representations; ii) Due to the absence of non-observed samples across multiple
views, the obtained prototypes of clusters might be unaligned and biased,
leading to incorrect fusion. To address the above issues, we propose a
Cross-view Partial Sample and Prototype Alignment Network (CPSPAN) for Deep
Incomplete Multi-view Clustering. Firstly, unlike existing contrastive-based
methods, we adopt pair-observed data alignment as 'proxy supervised signals' to
guide instance-to-instance correspondence construction among views. Then,
regarding of the shifted prototypes in IMVC, we further propose a prototype
alignment module to achieve incomplete distribution calibration across views.
Extensive experimental results showcase the effectiveness of our proposed
modules, attaining noteworthy performance improvements when compared to
existing IMVC competitors on benchmark datasets.
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