Subspace clustering is an effective method that has been successfully applied to many applications. Here, we propose a novel subspace clustering model for multi-view data using a latent representation termed Latent Multi-View Subspace Clustering (LMSC). Unlike most existing single-view subspace clustering methods, which directly reconstruct data points using original features, our method explores underlying complementary information from multiple views and simultaneously seeks the underlying latent representation. Using the complementarity of multiple views, the latent representation depicts data more comprehensively than each individual view, accordingly making subspace representation more accurate and robust. We proposed two LMSC formulations: linear LMSC (lLMSC), based on linear correlations between latent representation and each view, and generalized LMSC (gLMSC), based on neural networks to handle general relationships. The proposed method can be efficiently optimized under the Augmented Lagrangian Multiplier with Alternating Direction Minimization (ALM-ADM) framework. Extensive experiments on diverse datasets demonstrate the effectiveness of the proposed method.

中文翻译：

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广义潜在多视图子空间聚类


子空间聚类是一种有效的方法，已成功应用于许多应用。在这里，我们提出了一种新颖的多视图数据子空间聚类模型，使用称为潜在多视图子空间聚类（LMSC）的潜在表示。与大多数现有的使用原始特征直接重建数据点的单视图子空间聚类方法不同，我们的方法探索来自多个视图的潜在互补信息，并同时寻找潜在的潜在表示。利用多个视图的互补性，潜在表示比每个单独视图更全面地描述数据，从而使子空间表示更加准确和鲁棒。我们提出了两种 LMSC 公式：线性 LMSC (lLMSC)，基于潜在表示和每个视图之间的线性相关性；广义 LMSC (gLMSC)，基于神经网络来处理一般关系。该方法可以在交替方向最小化增强拉格朗日乘子（ALM-ADM）框架下进行有效优化。对不同数据集的广泛实验证明了所提出方法的有效性。