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, Volume 22, Issue 2, pp 673–688 | Cite as

Group sparse reduced rank regression for neuroimaging genetic study

  • Xiaofeng Zhu
  • Heung-Il Suk
  • Dinggang ShenEmail author
Part of the following topical collections:
  1. Special Issue on Deep vs. Shallow: Learning for Emerging Web-scale Data Computing and Applications


The neuroimaging genetic study usually needs to deal with high dimensionality of both brain imaging data and genetic data, so that often resulting in the issue of curse of dimensionality. In this paper, we propose a group sparse reduced rank regression model to take the relations of both the phenotypes and the genotypes for the neuroimaging genetic study. Specifically, we propose designing a graph sparsity constraint as well as a reduced rank constraint to simultaneously conduct subspace learning and feature selection. The group sparsity constraint conducts feature selection to identify genotypes highly related to neuroimaging data, while the reduced rank constraint considers the relations among neuroimaging data to conduct subspace learning in the feature selection model. Furthermore, an alternative optimization algorithm is proposed to solve the resulting objective function and is proved to achieve fast convergence. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset showed that the proposed method has superiority on predicting the phenotype data by the genotype data, than the alternative methods under comparison.


Reduced rank regression Subspace learning Feature selection Neuroimaging genetic study 



This work was supported in part by NIH grants (EB008374, AG041721, AG049371, AG042599, EB022880). X. Zhu was also supported by the National Natural Science Foundation of China (Grants No: 61573270 and 61876046); the Project of Guangxi Science and Technology (GuiKeAD17195062); and the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing. H.I. Suk was also supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451).


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Guangxi Key Lab of Multi-source Information Mining and SecurityGuangxi Normal UniversityGuilinPeople’s Republic of China
  2. 2.Institute of Natural and Mathematical SciencesMassey UniversityAucklandNew Zealand
  3. 3.Department of Brain and Cognitive EngineeringKorea UniversitySeoulKorea
  4. 4.BRIC Center of the University of North Carolina at Chapel HillChapel HillUSA

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