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    基于谱嵌入相似度的多层对比多视图聚类

    Multi-Layer Contrastive Multi-View Clustering via Spectral Embedding Similarity

    • 摘要: 多视图聚类通过无监督方式学习共识表示,将数据样本划分为不同类别,受到了广泛关注. 近年来,对比学习凭借其强大的跨视图特征对齐能力,在多视图聚类中展现出了巨大的潜力. 然而,现有对比多视图聚类方法存在正负样本划分的假阴性与假阳性问题,限制了聚类性能的进一步提升. 为解决这些问题,提出了基于谱嵌入相似度的多层对比多视图聚类方法. 通过引入基于谱嵌入相似度的多层对比学习机制,有效缓解了正负样本划分不准确带来的性能瓶颈. 首先,基于各视图潜在表示构造拉普拉斯矩阵,并利用其特征向量生成谱嵌入相似度矩阵. 其次,根据谱嵌入相似度矩阵提供的全局先验信息划分正负样本对,缓解了样本划分错误对潜在表示学习的干扰. 然后,设计跨视图对比损失来强化一致性表征和共享语义对齐. 最后,通过可学习权重自适应地融合各个视图的潜在表示,得到全局多视图表示,并在此基础上设计全局对比损失来强调多视图一致性信息. 在公开多视图数据集上的大量实验验证了所提方法的有效性.

       

      Abstract: Multi-view clustering, which learns consensus representations in an unsupervised manner to partition data samples into distinct categories, has gained widespread attention. In recent years, contrastive learning has demonstrated significant potential in multi-view clustering due to its powerful capability for cross-view feature alignment. However, existing contrastive multi-view clustering methods suffer from false negatives and false positives in positive-negative sample partitioning, limiting further improvements in clustering performance. To address these issues, we propose a Multi-layer Contrastive Multi-view Clustering via Spectral Embedding Similarity (MCSES). By introducing a multi-layer contrastive learning mechanism grounded in spectral embedding similarity, MCSES effectively mitigates the performance bottleneck caused by inaccurate sample partitioning. Specifically, Laplacian matrices are first constructed based on the latent representations of each view, and their eigenvectors are utilized to generate spectral embedding similarity matrices. Subsequently, positive and negative sample pairs are partitioned according to the global prior information provided by these spectral embedding similarity matrices, alleviating the interference of sample partitioning errors on latent representation learning. Then, a cross-view contrastive loss is designed to strengthen inter-view consistency and align shared semantics. Finally, the latent representations from each view are adaptively fused through learnable weights to obtain a global multi-view representation, upon which a global contrastive loss is designed to emphasize multi-view consensus information. Extensive experiments on public multi-view datasets validate the effectiveness of the proposed MCSES method.

       

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