Data Completion-Guided Unified Graph Learning for Incomplete Multi-View Clustering

Due to its heterogeneous property, multi-view data has been widely concerned over single-view data forperformance improvement. Unfortunately, some instances may be with partially available information becauseof some uncontrollable factors, for which the incomplete multi-view clustering (IMVC) problem is raised. IMVCaims to partition unlabeled incomplete multi-view data into their clusters by exploiting the heterogeneityof multi-view data and overcoming the difficulty of data loss. However, most existing IMVC methods likeBSV, MIC, OMVC, and IVC tend to conduct basic completion processing on the input data, without takingadvantage of the correlation between samples and information redundancy. To overcome the above issue,we propose one novel IMVC method named data completion-guided unified graph learning (DCUGL), whichcould complete the data of missing views and fuse multiple learned view-specific similarity matrices into oneunified graph. Specifically, we first reduce the dimension of the input data to learn multiple view-specificsimilarity matrices. By stacking all view-specific similarity matrices, DCUGL constructs a third-order tensorwith the low-rank constraint, such that sample correlation within and between views can be well explored.Finally, by dividing the original data into observed data and unobserved data, DCUGL can infer and completethe missing data according to the view-specific similarity matrices, and obtain a unified graph, which can be directly used for clustering. To solve the proposed model, we design an iterative algorithm, which is basedon the alternating direction method of multipliers framework. The proposed model proves to be superior bybenchmarking on six challenging datasets compared with state-of-the-art IMVC methods.