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This paper introduces DAS-PMVC, a novel framework for partial multi-view clustering that addresses the challenges posed by view misalignment and irrelevant samples. By employing a dual alignment strategy and structure enhancement techniques, the framework improves the consistency of latent space representations and enhances feature learning through multi-view graph convolutional networks. Experimental results indicate that DAS-PMVC significantly outperforms existing state-of-the-art methods, highlighting its effectiveness in clustering tasks across diverse datasets.
Clustering performance improves dramatically with DAS-PMVC, which effectively tackles the partial view alignment problem that plagues traditional multi-view methods.
In recent years, multi-view clustering has attracted widespread research interest. However, due to limitations in data collection devices, data across different views often suffer from misalignment, leading to the partial view alignment problem (PVAP). To mitigate the impact of view asymmetry and irrelevant samples, this paper proposes a framework for partial multi-view clustering via dual alignment and structure enhancement (DAS-PMVC), which leverages view structure consistency and semantic relevance. Specifically, DAS-PMVC includes three parts: \textbf{anchor graph structure alignment}, where sample joint embedding representations with consistent latent space are derived from anchor point relationships for initial view alignment; \textbf{structure-enhanced feature learning}, where the model learns view structure information through pretraining and combines multi-view graph convolutional networks to further extract deep latent features from the aligned graph structure to improve the discriminative power of representations; and \textbf{a dual alignment strategy}, where initial alignment is performed through the anchor graph in the pretraining phase, and contrastive learning loss and the Hungarian algorithm are introduced in the training phase to further optimize the alignment of latent features. Experimental results on various datasets demonstrate that the DAS-PMVC framework outperforms existing state-of-the-art methods in clustering performance, showcasing its effectiveness and superiority.
