{"type":"ScholarlyArticle","title":"Predicting Alzheimer's Disease Cognitive Assessment via Robust Low-Rank Structured Sparse Model","authors":[{"name":"Jie Xu","slug":"jie-xu","given":"Jie","family":"Xu","affiliation":"Xidian University , Xi'an 710071 , China; University of Texas at Arlington , USA"},{"name":"Cheng Deng","slug":"cheng-deng","given":"Cheng","family":"Deng","affiliation":"Xidian University , Xi'an 710071 , China"},{"name":"Xinbo Gao","slug":"xinbo-gao","given":"Xinbo","family":"Gao","affiliation":"Xidian University , Xi'an 710071 , China"},{"name":"Dinggang Shen","slug":"dinggang-shen","given":"Dinggang","family":"Shen","affiliation":"Department of Radiology and BRIC , UNC-Chapel Hill , USA"},{"name":"Heng Huang","slug":"heng-huang","given":"Heng","family":"Huang","affiliation":"University of Texas at Arlington , USA; Xidian University , Xi'an 710071 , China"}],"abstract":"Alzheimer's disease (AD) is a neurodegenerative disorder with slow onset, which could result in the deterioration of the duration of persistent neurological dysfunction. How to identify the informative longitudinal phenotypic neuroimaging markers and predict cognitive measures are crucial to recognize AD at early stage. Many existing models related imaging measures to cognitive status using regression models, but they did not take full consideration of the interaction between cognitive scores. In this paper, we propose a robust low-rank structured sparse regression method (RLSR) to address this issue. The proposed model simultaneously selects effective features and learns the underlying structure between cognitive scores by utilizing novel mixed structured sparsity inducing norms and low-rank approximation. In addition, an efficient algorithm is derived to solve the proposed non-smooth objective function with proved convergence. Empirical studies on cognitive data of the ADNI cohort demonstrate the superior performance of the proposed method.","doi":"10.24963/ijcai.2017/542","url":"https://ijcai.org/proceedings/2017/542","pdf":"https://ijcai.org/proceedings/2017/0542.pdf","markdown":"https://ijcai.org/proceedings/2017/542.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/542","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":542,"pages":{"first":3880,"last":3886},"keywords":["Multidisciplinary Topics and Applications: Computational Biology and e-Health","Multidisciplinary Topics and Applications: Brain Sciences"],"videos":[],"references":[{"position":1,"raw":"[Braak and Braak, 1991] Heiko Braak and Eva Braak. 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