Multi-view subspace clustering (MSC) has gained significant popularity due to its ability to overcome noise and bias present in single views by fusing information from multiple views. MSC enhances the accuracy and robustness of clustering. However, many existing MSC methods suffer from high computational costs and sub-optimal performance on large-scale datasets, since they often construct a fused graph directly from high-dimensional data and then apply spectral clustering. To address these challenges, we propose a framework called Landmark-based -Factorization Multi-view Subspace Clustering (LKMSC). Our framework tackles these issues by generating a small number of landmarks for each view, which form a landmark graph. We represent each entire view as a linear combination of these landmarks, where is the number of data points. To address inconsistencies that naturally occur in landmark graphs due to multiple views, we utilize Landmark Graphs Alignment. This technique incorporates both feature and structural information to capture the correspondence between landmarks. The aligned graphs are then factorized into consensus groups, emphasizing structural sparsity. LKMSC efficiently extracts features and reduces dimensionality of the input dataset. It eliminates the need for learning large-scale affinity matrix and feature decomposition. Our approach exhibits linear computational complexity and has demonstrated promising results in numerous experimental evaluations across a range of datasets. The source codes and datasets are available at .

中文翻译：

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基于地标的 k 因子分解多视图子空间聚类

多视图子空间聚类 (MSC) 因其能够通过融合多个视图的信息来克服单个视图中存在的噪声和偏差而受到广泛欢迎。 MSC增强了聚类的准确性和鲁棒性。然而，许多现有的 MSC 方法在大规模数据集上存在计算成本高和性能次优的问题，因为它们通常直接从高维数据构建融合图，然后应用谱聚类。为了应对这些挑战，我们提出了一个称为基于地标的分解多视图子空间聚类（LKMSC）的框架。我们的框架通过为每个视图生成少量地标来解决这些问题，这些地标形成了地标图。我们将每个整个视图表示为这些地标的线性组合，其中 是数据点的数量。为了解决由于多个视图而在地标图中自然出现的不一致问题，我们利用地标图对齐。该技术结合了特征和结构信息来捕获地标之间的对应关系。然后将对齐的图分解为共识组，强调结构稀疏性。 LKMSC 有效地提取特征并降低输入数据集的维度。它消除了学习大规模亲和力矩阵和特征分解的需要。我们的方法表现出线性计算复杂性，并在一系列数据集的大量实验评估中展示了有希望的结果。源代码和数据集可在 处获得。