Predicting Alzheimer's disease cognitive assessment via robust low-rank structured sparse model

Jie Xu, Cheng Deng, Xinbo Gao, Dinggang Shen, Heng Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publication26th International Joint Conference on Artificial Intelligence, IJCAI 2017
PublisherInternational Joint Conferences on Artificial Intelligence
Pages3880-3886
Number of pages7
ISBN (Electronic)9780999241103
Publication statusPublished - 2017
Externally publishedYes
Event26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia
Duration: 2017 Aug 192017 Aug 25

Other

Other26th International Joint Conference on Artificial Intelligence, IJCAI 2017
CountryAustralia
CityMelbourne
Period17/8/1917/8/25

Fingerprint

Neuroimaging
Deterioration
Imaging techniques

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Xu, J., Deng, C., Gao, X., Shen, D., & Huang, H. (2017). Predicting Alzheimer's disease cognitive assessment via robust low-rank structured sparse model. In 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 (pp. 3880-3886). International Joint Conferences on Artificial Intelligence.

Predicting Alzheimer's disease cognitive assessment via robust low-rank structured sparse model. / Xu, Jie; Deng, Cheng; Gao, Xinbo; Shen, Dinggang; Huang, Heng.

26th International Joint Conference on Artificial Intelligence, IJCAI 2017. International Joint Conferences on Artificial Intelligence, 2017. p. 3880-3886.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Xu, J, Deng, C, Gao, X, Shen, D & Huang, H 2017, Predicting Alzheimer's disease cognitive assessment via robust low-rank structured sparse model. in 26th International Joint Conference on Artificial Intelligence, IJCAI 2017. International Joint Conferences on Artificial Intelligence, pp. 3880-3886, 26th International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, 17/8/19.
Xu J, Deng C, Gao X, Shen D, Huang H. Predicting Alzheimer's disease cognitive assessment via robust low-rank structured sparse model. In 26th International Joint Conference on Artificial Intelligence, IJCAI 2017. International Joint Conferences on Artificial Intelligence. 2017. p. 3880-3886
Xu, Jie ; Deng, Cheng ; Gao, Xinbo ; Shen, Dinggang ; Huang, Heng. / Predicting Alzheimer's disease cognitive assessment via robust low-rank structured sparse model. 26th International Joint Conference on Artificial Intelligence, IJCAI 2017. International Joint Conferences on Artificial Intelligence, 2017. pp. 3880-3886
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