CASIA OpenIR  > 精密感知与控制研究中心  > 人工智能与机器学习
A Weighted Discriminative Dictionary Learning Method for Depression Disorder Classification using fMRI Data
Wang Xin1; Ren Yanshuang2; Yang Yehui1; Zhang Wensheng1; Neal N. Xiong3
2016
Conference Name2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom)
Conference Date2016-10-8
Conference PlaceAtlanta, GA, USA
AbstractIn this paper, we present a novel depression disorder classification algorithm, named weighted discriminative dictionary learning (WDDL), based on functional magnetic
resonance imaging (fMRI) data. The underlying relationship between samples and dictionary atoms is exploited by introducing an adaptive weighting scheme. Tested on fMRI data of 29 patients with depression and 29 healthy controls, our algorithm outperforms all other classification methods compared in this work. Furthermore, we detect the discriminative brain regions of patients which can reveal the pathogenesis of depression
disorder.

DOI10.1109/BDCloud-SocialCom-SustainCom.2016.97
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Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14845
Collection精密感知与控制研究中心_人工智能与机器学习
Corresponding AuthorZhang Wensheng
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.Department of Radiology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences
3.Department of Business and Computer Science, Southwestern Oklahoma State University
Recommended Citation
GB/T 7714
Wang Xin,Ren Yanshuang,Yang Yehui,et al. A Weighted Discriminative Dictionary Learning Method for Depression Disorder Classification using fMRI Data[C],2016.
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