HMCNet: A Novel Framework for Multi-View Clustering under Incomplete View Conditions
S. Bahadori
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Keywords: Probabilistic loss function, multi-view clustering, attention module, incomplete views, embedding based on nonlinear dimensionality reduction.,
Abstract :
Clustering multi-view data in the presence of incomplete views is one of the major challenges in machine learning and data mining. Existing methods often suffer from performance degradation when faced with high rates of incomplete views and are unable to extract common and consistent features between views. Considering the statistics and objective examples presented, which indicate the high prevalence of incomplete views in real applications (with rates between 10% and 50%) and the severe performance degradation of traditional methods in these conditions, the HMCNet model is introduced in this study. By utilizing the combination of attention module, nonlinear UMAP dimensionality reduction, and probability-based composite loss function, this model is able to preserve the local structures of the data and enhance the consistency between views even in the presence of high incompleteness rates (up to 50%). Extensive experiments were conducted on datasets for evaluation. The results showed that the proposed model consistently outperforms state-of-the-art methods in ACC, NMI, and Purity metrics. Also, the analyses of deletion experiments confirmed the importance of each component in improving the model performance. Overall, the findings of this research show that HMCNet can be used as a general and powerful framework for clustering multi-view data under incomplete view conditions and provide new avenues for real-world applications in various scientific and industrial fields.
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