Nonlinear Graph Fusion for Multi-modal Classification of Alzheimer’s Disease
Recent studies have demonstrated that biomarkers from multiple modalities contain complementary information for the diagnosis of Alzheimer’s disease (AD) and its prodromal stage mild cognitive impairment (MCI). In order to fuse data from multiple modalities, most previous approaches calculate a mixed kernel or a similarity matrix by linearly combining kernels or similarities from multiple modalities. However, the complementary information from multi-modal data are not necessarily linearly related. In addition, this linear combination is also sensitive to the weights assigned to each modality. In this paper, we propose a nonlinear graph fusion method to efficiently exploit the complementarity in the multi-modal data for the classification of AD. Specifically, a graph is first constructed for each modality individually. Afterwards, a single unified graph is obtained via a nonlinear combination of the graphs in an iterative cross diffusion process. Using the unified graphs, we achieved classification accuracies of 91.8% between AD subjects and normal controls (NC), 79.5% between MCI subjects and NC and 60.2% in a three-way classification, which are competitive with state-of-the-art results.
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