Joint Promotion of HNEMD and Contrastive Learning in Multi-view Alignment Clustering

Real-life data is generally represented by multi-view format because multi-view data can describe the charac-teristics of samples well through complementary information. However, when fusing complementary information, we cannot guarantee the alignment between the samples. For example, in the process of image transmission, the sequence may change, resulting in partial alignment between samples from different views. Therefore, achieving full alignment between views has garnered significant attention. Some end-to-end alignment and contrastive learning-based methods have been proposed; however, these methods still encounter challenges in mea-suring the similarity between high-dimensional samples and distinguishing among high-dimensional sample categories. In this paper, we propose a novel model, termed A Joint Facili-tation Alignment Model Based on Hyperbolic Non-Euclidean Mapping Distance and Contrastive Learning(HC-JFAM), to tackle them. Specifically, a two-layer encoder-decoder with an optimizer is explored to obtain better low-dimensional features. Furthermore, a new Hyperbolic Non-Euclidean Map-ping Distance (HNEMD) is designed to achieve cross-view sample alignment. Finally, the optimization function is re-designed to better discriminate between positive and negative pairs, and a new feature is obtained through weighted fusion for better clustering. Experimental results show that our pro-posed method improves the accuracy of sample alignment between views, and prove the effectiveness of HC-JFAM.