Deep Incomplete Multi-view Clustering via Multi-level Imputation and Contrastive Alignment.

Abstract

Deep incomplete multi-view clustering (DIMVC) aims to enhance clustering performance by capturing consistent information from incomplete multiple views using deep models. Most existing DIMVC methods typically employ imputation-based strategies to handle missing views before clustering. However, they often assume complete data availability across all views, overlook potential low-quality views, and perform imputation at a single data level, leading to challenges in accurately inferring missing data. To address these issues, we propose a novel imputation-based approach called Multi-level Imputation and Contrastive Alignment (MICA) to simultaneously improve imputation quality and boost clustering performance. Specifically, MICA employs an individual deep model for each view, which unifies view feature learning and cluster assignment prediction. It leverages the learned features from available instances to construct an adaptive cross-view graph for reliable view selection. Guided by these reliable views, MICA performs multi-level (feature-level, data-level, and reconstruction-level) imputation to preserve topological structures across levels and ensure accurate missing feature inference. The complete features are then used for discriminative cluster assignment learning. Additionally, an instance- and cluster-level contrastive alignment is conducted on the cluster assignments to further enhance semantic consistency across views. Experimental results show the effectiveness and superior performance of the proposed MICA method.