Spatial regularization based on support tensor machine for neuroimaging classification
- 10.2991/jimet-15.2015.23How to use a DOI?
- spatial regularization, support tensor machine, Alzheimer disease, neuroimaging
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.
- © 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/).
Cite this article
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 -