Incomplete multiview clustering with multiple contrastive learning and attention mechanism

Multi-view clustering is a complex and significant task in the fields of machine learning and data mining. Most of the existing multi-view clustering models are for views with complete information. However, data loss inevitably occurs during data collection and transmission, leading to the problems of partial individual unalignment (IU) and individual missing (IM). To address these challenges, the article proposes a framework called incomplete multi-view clustering with multiple contrastive learning and attention mechanism (IMCLAM). IMCLAM utilizes the maximization of mutual information of different views and enhance the separability of the representation through multiple contrastive learning and the fusion of specific low-dimensional representations into a joint representation through an attentional fusion layer. Moreover, the effect of negative samples is reduced by increasing the noise robustness loss. Experiments on four multi-view datasets demonstrate the effectiveness of IMCLAM on the task of multi-view clustering compared to six state-of the-art methods.