The content discusses the challenges faced in multi-view clustering when views are collected sequentially, introducing CCMVC-FSF as a solution. It highlights the importance of utilizing prior knowledge to enhance clustering performance and presents experimental results demonstrating the superiority of CCMVC-FSF over existing methods.
The authors propose a method that stores filtered structural information to guide clustering processes, addressing issues like privacy concerns and memory burden. By conducting contrastive learning and utilizing positive/negative sample settings, CCMVC-FSF outperforms other methods in terms of accuracy, normalized mutual information, and purity on various datasets.
Extensive experiments validate the effectiveness of CCMVC-FSF in overcoming the catastrophic forgetting problem in multi-view clustering. The proposed method shows robustness and superior performance compared to existing approaches, emphasizing the significance of leveraging prior knowledge for improved clustering outcomes.
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by Xinhang Wan,... at arxiv.org 03-05-2024
https://arxiv.org/pdf/2309.15135.pdfDeeper Inquiries