Mining Multi-View Clustering Space With Interpretable Space Search Constraint

Multi-view clustering can cluster signal samples from multiple views into groups. Currently, multi-view clustering fuses the information of different views into a low-dimensional space for clustering. However, the direct reduction of high-dimensional information to a very low-dimensional space leads to the loss of a lot of sample semantic information, while a higher dimension after dimensionality reduction may blur the clustering structure of samples. To tackle these problems, we propose a novel framework called Mining Multi-view Clustering Space with Interpretable Space Search Constraint to explore the clustering structure while preserving the low-dimensional space semantic information. Our method maps samples from the raw space to a low-dimensional space separated into consensus and private features. This allows us to explore the interpretability of samples in the low-dimensional space to achieve representative representations. After assembling these representations, guided fusion is carried out and a search constraint is imposed to achieve a more reasonable clustering structure. Finally, by dynamically screening positive and negative samples, the clustering performance of the clustering space is maximized by contrastive learning. Extensive experiments on public datasets demonstrate that our method achieves state-of-the-art clustering effectiveness.