Pseudo-Label Guided Incomplete Partial View-Aligned Clustering

Addressing the challenges of incomplete and misaligned data in multi-view learning is critical, giventhe inherent uncertainties and complexities of real-world data collection. These challenges often result in significant discrepancies in the quantity, quality, and completeness of data across different views. However, previous research has predominantly focused on addressing either incompleteness or misalignment in isolation. To address both incompleteness and misalignment simultaneously, we propose a novel model, Pseudo-Label Guided Incomplete Partial View-aligned Clustering (PGIPVC). Specifically, A pseudo-label acquisition module based on Cauchy divergence is proposed to preliminarily train the clustering structure of data in a single view, thereby obtaining pseudo-labels for each sample in the view. Subsequently, an incomplete partial alignment clustering module is designed to obtain discriminative latent representations through contrastive learning with positive-negative pairs selected based on KNN and pseudo-labeled samples. Extensive experiments on benchmark datasets demonstrate the superiority of our method compared to other state-of-the-art approaches.