With the widespread application of multi-view data in real-world scenarios, clustering with incomplete views has emerged as a significant challenge in machine learning. Traditional anchor graph-based clustering algorithms rely on complete instances to build the anchor graphs. This dependency leads to insufficient anchors for capturing the underlying data structure under high missing rates, while failing to fully leverage the benefits of anchors when missing rate is low. To address the limitations of traditional methods, including restricted anchor selection, inflexible weight assignment, and high computational complexity, this paper proposed an incomplete multi-view clustering algorithm based on a Sample-Complementary Anchor Graphs (IMVC-SAC). First, the algorithm introduces a cross-view anchor complementation mechanism, which adaptively selects anchors from both shared samples and view-specific samples to enhance data structure representation, particularly under high missing rates. Second, it establishes a missing pattern-aware weighting model that dynamically adjusts the contribution of each view to the similarity matrix based on the missing pattern and degree of the samples. Finally, by leveraging the properties of doubly stochastic non-negative matrix factorization, the time complexity of spectral clustering is reduced from cubic to linear with respect to the sample size. Experimental results on five public datasets demonstrate that the proposed IMVC-SAC algorithm outperforms state-of-the-art methods in clustering performance. Notably, it maintains robust and effective clustering even under high missing rates, validating its superiority.

Traditional multi-view clustering task is for complete data. However, in practical tasks, due to the limitation of the information acquisition method, some views tend to contain missing data, and this leads to the problem of incomplete multi-view clustering. In view of this problem, most of the existing clustering models are based on non-negative matrix factorization or distance graph, and their co-optimization strategy can easily make the performance of the solution insecure and the global structure can’t be fully characterized. In order to improve the performance of clustering graph, this paper proposed an incomplete multi-view clustering algorithm ALIMSC based on low-rank subspace clustering and anchor graph. The algorithm first obtained the benchmark similarity matrix of data by incomplete multi-view subspace clustering algorithm APMC based on anchor graph, which was embedded in the low-rank subspace clustering model. The similarity matrix was obtained by dimensionality ascending alignment and weighted fusion, and the final clustering graph was obtained by making the similarity matrix as consistent as possible with the benchmark similarity matrix. ALIMSC algorithm characterized the low-dimensional subspace distribution of high-dimensional data by imposing rank minimization constraint on the similarity matrix of each view and emphasized the subspace structure of the data on the basis of the original anchor graph, that is, the block diagonality reflected in the cluster graph. Experimental results on several public datasets show that the proposed algorithm outperforms the classical incomplete multi-view algorithms.