State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China.
State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China.
Neural Netw. 2022 Jun;150:102-111. doi: 10.1016/j.neunet.2022.03.009. Epub 2022 Mar 11.
The main purpose of multi-view subspace clustering is to reveal the intrinsic low-dimensional architecture of data points according to their multi-view characteristics. Exploring the potential relationship from views is one of the most essential research focuses of the multi-view task. To better utilize the complementary and consistency information from distinct views, we propose a novel robust subspace clustering approach based on consensus representation and orthogonal diversity (RMSCCO). A novel defined orthogonality term is adopted to improve the diversity and decrease the redundance of learning subspace representation. The consensus representation and subspace learning are integrated into one unified framework to characterize the consistency from views. The grouping-enhanced representation is utilized to maintain the local geometric architecture in the original data space. The ℓ-norm regularizer constraint to the noise is applied to improve the robustness. Finally, an optimization algorithm is exploited to solve RMSCCO with the Alternating Direction Method of Multipliers (ADMM). Extensive experimental results on six challenging datasets demonstrate that our approach has accomplished highly qualified performance.
多视图子空间聚类的主要目的是根据数据点的多视图特征揭示其内在的低维结构。探索视图之间的潜在关系是多视图任务的最基本研究重点之一。为了更好地利用来自不同视图的互补和一致性信息，我们提出了一种基于一致性表示和正交多样性的新的鲁棒子空间聚类方法（RMSCCO）。采用一种新的定义正交项来提高学习子空间表示的多样性并减少冗余性。一致性表示和子空间学习被集成到一个统一的框架中，以描述视图之间的一致性。分组增强表示用于保持原始数据空间中的局部几何结构。应用ℓ范数正则化约束噪声以提高鲁棒性。最后，利用交替方向乘子法（ADMM）来求解 RMSCCO 的优化算法。在六个具有挑战性的数据集上的广泛实验结果表明，我们的方法具有出色的性能。
