Abstract Multi-view clustering is a hot research topic in machine learning and pattern recognition, however, it remains high computational complexity when clustering multi-view data sets. Although a number of approaches have been proposed to accelerate the computational efficiency, most of them do not consider the data duality between features and samples. In this paper, we propose a novel co-clustering approach termed as Fast Multi-view Bilateral K-means (FMVBKM), which can implement clustering task on row and column of the input data matrix, simultaneously. Specifically, FMVBKM applies the relaxed K-means clustering technique to multi-view data clustering. In addition, to decrease information loss in matrix factorization, we further introduce a new co-clustering method named as Fast Multi-view Matrix Tri-Factorization (FMVMTF). Extensive experimental results on six benchmark data sets show that the proposed two approaches not only have comparable clustering performance but also present the high computational efficiency, in comparison with state-of-the-art multi-view clustering methods.

中文翻译：

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通过快速矩阵分解的自动加权多视图协同聚类

摘要 多视图聚类是机器学习和模式识别领域的一个热门研究课题，但在对多视图数据集进行聚类时，其计算复杂度仍然很高。尽管已经提出了许多方法来加速计算效率，但大多数都没有考虑特征和样本之间的数据对偶性。在本文中，我们提出了一种新的协同聚类方法，称为快速多视图双边 K 均值（FMVBKM），它可以同时在输入数据矩阵的行和列上实现聚类任务。具体来说，FMVBKM 将宽松 K 均值聚类技术应用于多视图数据聚类。此外，为了减少矩阵分解中的信息损失，我们进一步引入了一种新的协同聚类方法，称为快速多视图矩阵三分解（FMVMTF）。