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 k-Factorization Multi-view Subspace Clustering (LKMSC). Our framework tackles these issues by generating a small number of landmarks p≪n for each view, which form a landmark graph. We represent each entire view as a linear combination of these landmarks, where n 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 k 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 https://github.com/FY0109/LKMSC.