In recent years, multi-view subspace clustering has achieved impressive performance due to the exploitation of complementary imformation across multiple views. However, multi-view data can be very complicated and are not easy to cluster in real-world applications. Most existing methods operate on raw data and may not obtain the optimal solution. In this work, we propose a novel multi-view clustering method named smoothed multi-view subspace clustering (SMVSC) by employing a novel technique, i.e., graph filtering, to obtain a smooth representation for each view, in which similar data points have similar feature values. Specifically, it retains the graph geometric features through applying a low-pass filter. Consequently, it produces a ``clustering-friendly" representation and greatly facilitates the downstream clustering task. Extensive experiments on benchmark datasets validate the superiority of our approach. Analysis shows that graph filtering increases the separability of classes.
翻译:近年来,多视图子空间群集取得了令人印象深刻的成绩,因为利用了多种观点之间的互补变形,然而,多视图数据可能非常复杂,而且不容易在现实世界的应用中分组,大多数现有方法都是在原始数据上运作,可能得不到最佳解决办法。在这项工作中,我们建议采用一种称为平滑多视图子空间群集(SMVSC)的新颖的多视图群集方法,采用一种新技术,即图示过滤法,为每个类似数据点具有相似特征值的视图取得平稳的表示法。具体地说,它通过应用低通道过滤器保留图形几何特征。因此,它产生了一种“有利于集群的”代表法,极大地便利了下游集任务。关于基准数据集的广泛实验证实了我们方法的优越性。分析表明,图形过滤法增加了各类的分离性。