Tensorial Kernel Based on Spatial Structure Information for Neuroimaging Classification

YingJiang WU  BenYong LIU  

IEICE TRANSACTIONS on Information and Systems   Vol.E100-D    No.6    pp.1380-1383
Publication Date: 2017/06/01
Publicized: 2017/02/23
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016EDL8225
Type of Manuscript: LETTER
Category: Pattern Recognition
neuroimaging,  spatial regularization,  tensorial kernel function,  multiple kernel learning,  

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Recently, a high dimensional classification framework has been proposed to introduce spatial structure information in classical single kernel support vector machine optimization scheme for brain image analysis. However, during the construction of spatial kernel in this framework, a huge adjacency matrix is adopted to determine the adjacency relation between each pair of voxels and thus it leads to very high computational complexity in the spatial kernel calculation. The method is improved in this manuscript by a new construction of tensorial kernel wherein a 3-order tensor is adopted to preserve the adjacency relation so that calculation of the above huge matrix is avoided, and hence the computational complexity is significantly reduced. The improvement is verified by experimental results on classification of Alzheimer patients and cognitively normal controls.