Adaptive and structured graph learning for semi-supervised clustering

Task-radMBNet: An Improved Task-Driven Dynamic Graph Sparsity Pattern Radiomics-Based Morphological Brain Network for Alzheimer's Disease Characterization

Background: The study of task-driven dynamic adaptive graph sparsity patterns in Alzheimer's disease (AD) analysis is of great importance, as it allows for better focus on regions and connections of interest and enhances task sensitivity. Methods: In this study, we introduced a task-driven dynamic adaptive graph sparsity model (called task-driven radiomics-based morphological brain network [Task-radMBNet]) for AD diagnosis based on radiomics-based morphological brain network (radMBN). Specifically, the Task-radMBNet was established by devising a connectivity feature-based graph convolutional network (GCN) channel (called a connectivity-GCN channel) and a radiomics feature-based GCN channel (called a radiomics-GCN channel), where the two GCN channels shared a same dynamic sparse brain network in graph convolution but worked for different aims separately. The connectivity-GCN channel dynamically learned the graph's sparse topology that best suits the target task, while the radiomics-GCN channel combined radiomics node features and dynamic topology to improve AD diagnostic accuracy. Results: The Task-radMBNet achieved superior classification accuracy of 87.8% and 86.0% in early AD diagnosis across a total of 1273 subjects within the AD Neuroimaging Initiative and European Diffusion Tensor Imaging (DTI) Study on Dementia databases. We also visualized the topology heat map and important connectivity under different network sparse settings. Conclusions: The results demonstrated significant promise in the diagnosis of neurological disorders by integrating Task-radMBNet with radMBN.

A robust semi-supervised regressor with correntropy-induced manifold regularization and adaptive graph

For semi-supervised regression tasks, most existing methods ignore the impact of noise. However, the data inevitably contain noise. Therefore, this study proposes a novel correntropy-induced semi-supervised regression (CSSR) method that mitigates the adverse effects of noise. To implement the robustness of CSSR, a novel correntropy-induced manifold regularization (CMR) and a correntropy-induced adaptive graph (CAG) are designed. Specifically, CMR is inspired by the principles of correntropy and aims to learn a graph representation, whereas CAG inherits the robust characteristics of the correntropy metric and adaptively constructs an adjacency matrix. Finally, by incorporating CMR, CAG, and the correntropy-induced loss seamlessly, CSSR demonstrates the ability to deliver promising joint performance. The final solution of CSSR is achieved through an iterative process. Moreover, we validated the convergence of CSSR through a combination of theoretical analyses and empirical experiments. The experimental evaluation encompassed three synthetic, 15 benchmark, and two image datasets. The findings demonstrate that CSSR surpasses similar methods in the realm of semi-supervised regression tasks, demonstrating its effectiveness and robustness.