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    刘金花, 王洋, 钱宇华. 基于谱结构融合的多视图聚类[J]. 计算机研究与发展, 2022, 59(4): 922-935. DOI: 10.7544/issn1000-1239.20200875
    引用本文: 刘金花, 王洋, 钱宇华. 基于谱结构融合的多视图聚类[J]. 计算机研究与发展, 2022, 59(4): 922-935. DOI: 10.7544/issn1000-1239.20200875
    Liu Jinhua, Wang Yang, Qian Yuhua. Multi-View Clustering with Spectral Structure Fusion[J]. Journal of Computer Research and Development, 2022, 59(4): 922-935. DOI: 10.7544/issn1000-1239.20200875
    Citation: Liu Jinhua, Wang Yang, Qian Yuhua. Multi-View Clustering with Spectral Structure Fusion[J]. Journal of Computer Research and Development, 2022, 59(4): 922-935. DOI: 10.7544/issn1000-1239.20200875

    基于谱结构融合的多视图聚类

    Multi-View Clustering with Spectral Structure Fusion

    • 摘要: 多视图聚类需要将多个视图的数据信息进行融合表示,是一项重要且具有挑战的任务.至今仍存在2个难解的问题:1)如何将多视图信息有效融合,减少信息丢失;2)如何将图学习和谱聚类同时进行,避免2步策略带来次优化结果.由于数据本身存在噪声并且各视图数据差异较大,在数据空间进行融合可能会造成重要信息的损失;另外,考虑到不同视图的数据应具有相同的聚类结构.为此提出基于谱结构融合的多视图聚类模型,将各视图信息在谱嵌入阶段实施融合,一方面避免了噪声和各视图数据差异的影响,另一方面融合的部位和方式更自然,减少了融合阶段信息的丢失.另外,该模型利用子空间自表示进行图学习,有效地将图学习和谱聚类整合到统一框架中进行联合优化学习.在5个真实数据集上的实验表明了模型的有效性和优越性.

       

      Abstract: Multi-view clustering is an important and challenged task due to the difficulty of integrating information from diverse views. After years of research it still faces two challenged questions to date. First, how to integrate heterogeneous information from different views effectively and reduce information loss. Second, how to perform graph learning and spectral clustering simultaneously, avoiding suboptimal clustering results caused by two-step strategy. Integrating heterogeneous information in the data space may cause significant information loss because of unavoidable noise hidden in the data itself or inconsistency among views. Moreover, we consider the case that different views admit the same cluster structure. To fill these gaps, a novel multi-view clustering model with spectral structure fusion is proposed, which fuses the information in the stage of spectral embedding. On the one hand, it avoids the influence of noise and difference of data from diverse views; on the other hand, the fusion position and method are more natural, which reduces the loss of information in the fusion stage. Besides, the model utilizes subspace self-representation for graph learning and integrates graph learning and spectral clustering into a unified framework effectively by joint optimization learning. Experiments on five widely used data sets confirm the superiority and validity of the proposed method.

       

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