Consensus Guided Multi-View Clustering
Consensus Guided Multi-View Clustering
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DOI：
10.1145/3182384
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发表时间：
2018-04
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影响因子：
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通讯作者：
Hongfu Liu;Y. Fu
中科院分区：
文献类型：
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作者：
Hongfu Liu;Y. Fu
近几十年来，由于从多个传感器捕获的数据越来越多，大量新兴技术使人工智能领域蓬勃发展。这些多视图数据提供了比传统的单视图数据更丰富的信息。为特定任务融合异构信息是多视图学习的核心部分，特别是对于多视图聚类。虽然已经提出了许多多视图聚类算法，但大多数学者都专注于寻找不同视图的公共空间，但遗憾的是，忽略了集成聚类的划分级别的好处。然而，对于集成聚类，来自每个视图的各个分区与最终共识分区之间没有交互。为了填补这一差距，我们提出了一个共识引导的多视图聚类（CMVC）框架，它包括从每个视图生成基本分区和以交互方式融合共识聚类，即，一致性聚类指导基本分区的生成，并且高质量的基本分区也对一致性聚类有积极贡献。我们设计了一个非平凡的优化解决方案，制定CMVC到两个迭代的k-means聚类的近似计算。此外，CMVC的推广为不同场景提供了丰富的可行性，而不完全多视图聚类的CMVC扩展进一步验证了其在实际应用中的有效性。实验结果表明，CMVC算法在聚类有效性、对重要参数的鲁棒性和对不完整多视图数据的鲁棒性等方面优于其他多视图聚类算法。
In recent decades, tremendous emerging techniques thrive the artificial intelligence field due to the increasing collected data captured from multiple sensors. These multi-view data provide more rich information than traditional single-view data. Fusing heterogeneous information for certain tasks is a core part of multi-view learning, especially for multi-view clustering. Although numerous multi-view clustering algorithms have been proposed, most scholars focus on finding the common space of different views, but unfortunately ignore the benefits from partition level by ensemble clustering. For ensemble clustering, however, there is no interaction between individual partitions from each view and the final consensus one. To fill the gap, we propose a Consensus Guided Multi-View Clustering (CMVC) framework, which incorporates the generation of basic partitions from each view and fusion of consensus clustering in an interactive way, i.e., the consensus clustering guides the generation of basic partitions, and high quality basic partitions positively contribute to the consensus clustering as well. We design a non-trivial optimization solution to formulate CMVC into two iterative k-means clusterings by an approximate calculation. In addition, the generalization of CMVC provides a rich feasibility for different scenarios, and the extension of CMVC with incomplete multi-view clustering further validates the effectiveness for real-world applications. Extensive experiments demonstrate the advantages of CMVC over other widely used multi-view clustering methods in terms of cluster validity, and the robustness of CMVC to some important parameters and incomplete multi-view data.