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Efficient correntropy-based multi-view clustering with alignment discretization
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PRE
AI
DOI：10.1016/j.knosys.2024.111768.png)
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Abstract
En 中文
Multiview clustering (MVC) has attracted considerable attention owing to its remarkable capacity to reconcile diverse information from multiple perspectives. However, traditional MVC generally has a narrow scope of application owing to its limited efficiency. Consequently, various efficient MVC (EMVC) methods have emerged recently. Despite their promising performance, these EMVC methods still have several unresolved issues: (1) They suffer from reduced effectiveness caused by representation non-alignment across views and information mismatch between stages, and (2) they fail to efficiently resist complex noises and outliers. To address these issues, we propose an efficient correntropy-based multiview clustering method with alignment discretization (ECMCAD). Specifically, a correntropy-based multipartition learning model was developed to efficiently learn view-specific robust partition-level representations. Additionally, a novel alignment discretization strategy was designed to align the learned cross-view representations into a consensus discrete indicator to integrate representation learning, representation alignment, and discrete label acquisition into a unified framework. Furthermore, an efficient alternating optimization method was developed to solve the model. Numerous experiments illustrated the superiority of the proposed method over state-of-the-art baselines.
Keywords:
Correntropy
Multi-view clustering
Representation alignment
Discrete representation learning
Journal
K
IF:
7.6
Papers:
1.3W
Citations:
4.5W
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