Spatial regularization based on support tensor machine for neuroimaging classification

DOI

10.2991/jimet-15.2015.23How to use a DOI?

Keywords

spatial regularization, support tensor machine, Alzheimer disease, neuroimaging

Abstract

Recently, a high dimensional classification framework has been proposed to introduce spatial and anatomical priors in support vector machine (SVM) optimization scheme for brain image analysis. However, classical SVM has to convert 3D discrete brain images naturally represented by higher-order tensors to one-dimensional vectors in order to meet the input requirements. This traditional method destroys the natural structure and correlation in the original data, and generates high dimensional vectors. In this manuscript, the method is improved by a modified support tensor machine (STM) algorithm to make full use of spatial prior and the inherent information of tensor. The new approach reduces memory requirements and computational complexity significantly, and it is comparably demonstrated by experimental results on classification of Alzheimer patients and elderly controls.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yingjiang Wu
AU  - Benyong Liu
PY  - 2015/12
DA  - 2015/12
TI  - Spatial regularization based on support tensor machine for neuroimaging classification
BT  - Proceedings of the 2015 Joint International Mechanical, Electronic and Information Technology Conference
PB  - Atlantis Press
SP  - 126
EP  - 131
SN  - 2352-538X
UR  - https://doi.org/10.2991/jimet-15.2015.23
DO  - 10.2991/jimet-15.2015.23
ID  - Wu2015/12
ER  -