From Incompleteness to Unity: A Framework for Multi-view Clustering with Missing Values

The assumption of data completeness plays a significant role in the effectiveness of current Multi-view Clustering (MVC) methods. However, data collection and transmission would unavoidably breach this assumption, resulting in the Partially Data-missing Problem (PDP). A common remedy is to first impute missing values and then conduct MVC methods, which may cause performance degeneration due to inaccurate imputation. To address these issues in PDP, we introduce an imputation-free framework that utilizes a matrix correction technique, employing a novel two-stage strategy termed ’correction-clustering’. In the first stage, we correct distance matrices derived from incomplete data and compute affinity matrices. Following this, we integrate them with affinity-based MVC methods. This approach circumvents the uncertainties associated with inaccurate imputations, enhancing clustering performance. Comprehensive experiments show that our method outperforms traditional imputation-based techniques, yielding superior clustering results across various levels of missing data.