Exploring unified cross-view hypergraph generation for multi-view semi-supervised classification.

Abstract

Graph structure is widely used in the field of multi-view learning. Hypergraph which is a kind of extension of graph can capture the higher-order relationships of nodes in a better way. However, most existing hypergraph-based models are based on the assumption that hypergraph structures are readily available, which is untenable in most cases. In order to alleviate this problem, we propose the learnable unified hypergraph dynamic system framework, a novel approach in unified cross-view hypergraph structure generation tailored for multi-view semi-supervised classification. Specifically, we introduce four strategies for unified cross-view hypergraph generation and propose a mechanism for generating learnable unified cross-view hypergraph. Furthermore, we utilize a dynamic diffusion model to dynamically learn unified hypergraph structure which can achieve better performance in multi-view semi-supervised classification tasks. Extensive experimental results on various real datasets show that the proposed method outperforms other state-of-the-art multi-view algorithms.