Nowadays, non-negative matrix factorization (NMF) based cluster analysis for multi-view data shows impressive behavior in machine learning. Usually, multi-view data have complementary information from various views. The main concern behind the NMF is how to factorize the data to achieve a significant clustering solution from these complementary views. However, NMF does not focus to conserve the geometrical structures of the data space. In this article, we intensify on the above issue and evolve a new NMF clustering method with manifold regularization for multi-view data. The manifold regularization factor is exploited to retain the locally geometrical structure of the data space and gives extensively common clustering solution from multiple views. The weight control term is adopted to handle the distribution of each view weight. An iterative optimization strategy depended on multiplicative update rule is applied on the objective function to achieve optimization. Experimental analysis on the real-world datasets are exhibited that the proposed approach achieves better clustering performance than some state-of-the-art algorithms.

如今，基于非负矩阵分解（NMF）的多视图数据聚类分析显示了机器学习中令人印象深刻的行为。通常，多视图数据具有来自各种视图的补充信息。NMF背后的主要关注点是如何从这些互补的角度对数据进行分解以实现重要的聚类解决方案。但是，NMF并不专注于保存数据空间的几何结构。在本文中，我们着重解决上述问题，并开发了一种新的NMF聚类方法，该方法具有用于多视图数据的流形正则化。利用流形正则化因子来保留数据空间的局部几何结构，并从多个角度提供广泛通用的聚类解决方案。权重控制项用于处理每个视图权重的分布。将依赖于乘法更新规则的迭代优化策略应用于目标函数以实现优化。通过对真实数据集的实验分析表明，与某些最新算法相比，该方法具有更好的聚类性能。