Chen Man-Sheng, Huang Ling, Wang Chang-Dong, Huang Dong, Yu Philip S
IEEE Trans Cybern. 2022 Aug;52(8):7655-7668. doi: 10.1109/TCYB.2020.3035043. Epub 2022 Jul 19.
Multiview subspace clustering (MVSC) is a recently emerging technique that aims to discover the underlying subspace in multiview data and thereby cluster the data based on the learned subspace. Though quite a few MVSC methods have been proposed in recent years, most of them cannot explicitly preserve the locality in the learned subspaces and also neglect the subspacewise grouping effect, which restricts their ability of multiview subspace learning. To address this, in this article, we propose a novel MVSC with grouping effect (MvSCGE) approach. Particularly, our approach simultaneously learns the multiple subspace representations for multiple views with smooth regularization, and then exploits the subspacewise grouping effect in these learned subspaces by means of a unified optimization framework. Meanwhile, the proposed approach is able to ensure the cross-view consistency and learn a consistent cluster indicator matrix for the final clustering results. Extensive experiments on several benchmark datasets have been conducted to validate the superiority of the proposed approach.
多视图子空间聚类（MVSC）是一种最近兴起的技术，旨在发现多视图数据中的潜在子空间，从而基于所学子空间对数据进行聚类。尽管近年来已经提出了不少MVSC方法，但其中大多数方法无法在所学子空间中明确保留局部性，并且还忽略了子空间分组效应，这限制了它们进行多视图子空间学习的能力。为了解决这个问题，在本文中，我们提出了一种具有分组效应的新型MVSC（MvSCGE）方法。具体而言，我们的方法通过平滑正则化同时学习多个视图的多个子空间表示，然后借助统一的优化框架利用这些所学子空间中的子空间分组效应。同时，所提出的方法能够确保跨视图一致性，并为最终聚类结果学习一个一致的聚类指示矩阵。我们在几个基准数据集上进行了大量实验，以验证所提出方法的优越性。
