Dual hypergraphs with feature weighted and latent space learning for the of Alzheimer's disease

In recent years of research on the diagnosis of Alzheimer's disease, capturing data relationships can help improve model performance. However, the simple graph structure can only capture pairwise relationships between data, which cannot model the complex data relationships in real situations. In addition, the redundant features and noise from the original data space also harm the model performance. Dual hypergraphs with feature-weighted and latent space learning (DHFWLSL) method for Alzheimer's disease diagnosis are proposed to solve the abovementioned problems. Hypergraphs are used to capture higher-order data relationships and construct hypergraphs for both sample and feature relationships to fully utilize the auxiliary ability of data relationships for model classification. In order to remove noise and redundant features from the original data space, latent space learning is utilized to project the original data into the latent space, and then data is projected into the label space for diagnosis. Finally, a feature-weighted strategy helps to improve the model performance further. An alternating optimization algorithm with the proof of convergence is used to solve the proposed model. Sufficient experiments support the validity and advantages of the proposed model.