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. Author manuscript; available in PMC: 2018 Feb 1.
Published in final edited form as: Med Image Anal. 2016 Nov 16;36:123–134. doi: 10.1016/j.media.2016.11.002

Figure 1.

Figure 1

Illustration of the proposed view-aligned hypergraph learning method, where subjects from the baseline ADNI-1 database are taken as examples. Subjects are divided into M (M = 6 in this study) views according to the data availability of a certain combination of modalities, where each view contains subjects with complete data of combined modalities. We then compute the distances among subjects via a sparse representation model, and construct one hypergraph in each view space. A view-aligned hypergraph classification method is further proposed, followed by a multi-view label fusion method to make a final classification decision.