Alzheimer's disease multiclass diagnosis via multimodal neuroimaging embedding feature selection and fusion

Alzheimer's disease (AD) will become a global burden in the coming decades according to the latest statistical survey. How to effectively detect AD or MCI (mild cognitive impairment) using reliable biomarkers and robust machine learning methods has become a challenging problem. In this study, we propose a novel AD multiclass classification framework with embedding feature selection and fusion based on multimodal neuroimaging. The framework has three novel aspects: (1) An l(2,1)-norm regularization term combined with the multiclass hinge loss is used to naturally select features across all the classes in each modality. (2) To fuse the complementary information contained in each modality, an l(p)-norm (1 < p < infinity) regularization term is introduced to combine different kernels to perform multiple kernel learning to avoid a sparse kernel coefficient distribution, thereby effectively exploiting complementary modalities. (3) A theorem that transforms the multiclass hinge loss minimization problem using the l(2,1)-norm and l(p)-norm regularizations to a previous solvable optimization problem and its proof are given. Additionally, it is theoretically proved that the optimization process converges to the global optimum. Extensive comparison experiments and analysis support the promising performance of the proposed method.

Automated summary

The study introduces a novel multiclass classification framework for AD based on multimodal neuroimaging with embedded feature selection and fusion. By using regularization and optimization techniques, it achieves feature selection and fusion, proving the convergence of the optimization process. Experimental results demonstrate the method's promising performance.