Effectively utilizing incomplete multi-modality data for diagnosis of Alzheimer’s disease (AD) is still an area of active research. Several multi-view learning methods have recently been developed to deal with missing data,with each view corresponding to a specific modality or a combination of several modalities. However,existing methods usually ignore the underlying coherence among views,which may lead to suboptimal learning performance. In this paper,we propose a view-aligned hypergraph learning (VAHL) method to explicitly model the coherence among the views. Specifically,we first divide the original data into several views based on possible combinations of modalities,followed by a sparse representation based hypergraph construction process in each view. A view-aligned hypergraph classification (VAHC) model is then proposed,by using a view-aligned regularizer to model the view coherence. We further assemble the class probability scores generated from VAHC via a multi-view label fusion method to make a final classification decision. We evaluate our method on the baseline ADNI-1 database having 807 subjects and three modalities (i.e.,MRI,PET,and CSF). Our method achieves at least a 4.6% improvement in classification accuracy compared with state-of-the-art methods for AD/MCI diagnosis.
CITATION STYLE
Liu, M., Zhang, J., Yap, P. T., & Shen, D. (2016). Diagnosis of Alzheimer’s disease using view-aligned hypergraph learning with incomplete multi-modality data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9900 LNCS, pp. 308–316). Springer Verlag. https://doi.org/10.1007/978-3-319-46720-7_36
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