Abstract

With the development of data collection technologies, a significant volume of multiview data has appeared, and their clustering has become topical. Most methods of multiview clustering assume that all views are fully observable. However, in many cases this is not the case. Several tensor methods have been proposed to deal with incomplete multiview data. However, the traditional tensor norm is computationally expensive, and such methods generally cannot handle undersampling and imbalances of various views. A new method for clustering incomplete multiview data is proposed. A new tensor norm is defined to reconstruct the connectivity graph, and the graphs are regularized to a consistent low-dimensional representation of patterns. The weights are then iteratively updated for each view. Compared to the existing ones, the proposed method not only determines the consistency between views but also obtains a low-dimensional representation of the samples using the resulting projection matrix. An efficient optimization algorithm based on the method of indefinite Lagrange multipliers is developed for the solution. The experimental results on four data sets demonstrate the effectiveness of the method.

中文翻译：

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通过连通图张量重构改进不完全多视图聚类

摘要

随着数据采集技术的发展，出现了大量的多视图数据，它们的聚类已成为热门话题。大多数多视图聚类方法都假设所有视图都是完全可观察的。然而，在许多情况下情况并非如此。已经提出了几种张量方法来处理不完整的多视图数据。然而，传统的张量范数计算量大，并且此类方法通常无法处理欠采样和各种视图的不平衡。提出了一种新的不完整多视图数据聚类方法。定义新的张量范数来重建连接图，并将图正则化为模式的一致低维表示。然后针对每个视图迭代更新权重。与现有的相比，所提出的方法不仅确定视图之间的一致性，而且还使用所得的投影矩阵获得样本的低维表示。为解决该问题，开发了一种基于不定拉格朗日乘子法的高效优化算法。四个数据集的实验结果证明了该方法的有效性。