{"type":"ScholarlyArticle","title":"Multi-graph Fusion for Functional Neuroimaging Biomarker Detection","authors":[{"name":"Jiangzhang Gan","slug":"jiangzhang-gan","given":"Jiangzhang","family":"Gan","affiliation":"Center for Future Media and School of Computer Science and Technology , University of Electronic Science and Technology of China , Chengdu 611731 , China; School of Natural and Computational Science , Massey University Auckland Campus , New Zealand"},{"name":"Xiaofeng Zhu","slug":"xiaofeng-zhu","given":"Xiaofeng","family":"Zhu","affiliation":"Center for Future Media and School of Computer Science and Technology , University of Electronic Science and Technology of China , Chengdu 611731 , China; School of Natural and Computational Science , Massey University Auckland Campus , New Zealand; Sichuan Artificial Intelligence Research Institute , Yibin 644000 , China"},{"name":"Rongyao Hu","slug":"rongyao-hu","given":"Rongyao","family":"Hu","affiliation":"School of Natural and Computational Science , Massey University Auckland Campus , New Zealand"},{"name":"Yonghua Zhu","slug":"yonghua-zhu","given":"Yonghua","family":"Zhu","affiliation":"School of Natural and Computational Science , Massey University Auckland Campus , New Zealand"},{"name":"Junbo Ma","slug":"junbo-ma","given":"Junbo","family":"Ma","affiliation":"School of Medicine and"},{"name":"Ziwen Peng","slug":"ziwen-peng","given":"Ziwen","family":"Peng","affiliation":"College of Psychology and Sociology , Shenzhen University , Shenzhen 518060 , China"},{"name":"Guorong Wu","slug":"guorong-wu","given":"Guorong","family":"Wu","affiliation":"School of Medicine and; Department of Computer Science , University of North Carolina at Chapel Hill , NC 27599 , USA"}],"abstract":"Brain functional connectivity analysis on fMRI data could improve the understanding of human brain function. However, due to the influence of the inter-subject variability and the heterogeneity across subjects, previous methods of functional connectivity analysis are often insufficient in capturing disease-related representation so that decreasing disease diagnosis performance.\r\nIn this paper, we first propose a new multi-graph fusion framework to fine-tune the original representation derived from Pearson correlation analysis, and then employ L1-SVM on fine-tuned representations to conduct joint brain region selection and disease diagnosis for avoiding the issue of the curse of dimensionality on high-dimensional data. The multi-graph fusion framework automatically learns the connectivity number for every node (i.e., brain region) and integrates all subjects in a unified framework to output homogenous and discriminative representations of all subjects. Experimental results on two real data sets, i.e., fronto-temporal dementia (FTD) and obsessive-compulsive disorder (OCD), verified the effectiveness of our proposed framework, compared to state-of-the-art methods.","doi":"10.24963/ijcai.2020/81","url":"https://ijcai.org/proceedings/2020/81","pdf":"https://ijcai.org/proceedings/2020/0081.pdf","markdown":"https://ijcai.org/proceedings/2020/81.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/81","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Computer Vision","track":"Main track","number":81,"pages":{"first":580,"last":586},"keywords":["Computer Vision: Biomedical Image Understanding","Machine Learning: Classification","Data Mining: Classification, Semi-Supervised Learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25015/file_25015.m3u8","length_sec":268,"provider":"hls"}],"references":[{"position":1,"raw":"[Bertsekas, 1995] Dimitri P Bertsekas. 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