Abstract
With the increasing prevalence of multi-view data in practical applications, multi-view clustering has become popular due to its ability to integrate complementary information from multiple views to enhance clustering accuracy and robustness. However, existing multi-view clustering methods easily suffer from the following limitations: 1) Most existing methods assume that each sample can be well represented in the original data space, but real-world data inevitably contains redundancy and noise; 2) Existing comparative methods have sampling bias when selecting negative samples; 3) The pseudo-labels generated by model iteration are underused. To address these challenges, this paper proposes a self-supervised multi-view clustering framework with graph filtering and contrast fusion named SMCGC. Specifically, SMCGC first eliminates redundancies and noise in the original data through graph filtering and then uses contrast fusion to enhance the discriminative ability of the samples. This approach avoids the potential challenges related to negative sample selection and does not require the construction of positive and negative samples. For pseudo-labels, the framework utilizes self-supervised mechanisms to guide model operation and optimize cluster representation. Extensive experimental results on six benchmark datasets demonstrate the effectiveness of the proposed SMCGC against the existing state-of-the-art methods.
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This research is supported by the Program for Young and Middle-aged Academic and Technical Reserve Leaders of Yunnan Province (202205AC160033).
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Lu, Y., Kong, B., Du, G., Bao, C., Zhou, L., Chen, H. (2023). Self-supervised Multi-view Clustering Framework with Graph Filtering and Contrast Fusion. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14179. Springer, Cham. https://doi.org/10.1007/978-3-031-46674-8_9
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