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, Volume 22, Issue 2, pp 907–925 | Cite as

Low-rank dimensionality reduction for multi-modality neurodegenerative disease identification

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

Abstract

In this paper, we propose a novel dimensionality reduction method of taking the advantages of the variability, sparsity, and low-rankness of neuroimaging data for Alzheimer’s Disease (AD) classification. We first take the variability of neuroimaging data into account by partitioning them into sub-classes by means of clustering, which thus captures the underlying multi-peak distributional characteristics in neuroimaging data. We then iteratively conduct Low-Rank Dimensionality Reduction (LRDR) and orthogonal rotation in a sparse linear regression framework, in order to find the low-dimensional structure of high-dimensional data. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset showed that our proposed model helped enhance the performances of AD classification, outperforming the state-of-the-art methods.

Keywords

Alzheimer’s Disease (AD) Feature selection Subspace learning 

Notes

Acknowledgments

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); the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing; Strategic Research Excellence Fund at Massey University; and Marsden Fund of New Zealand (grant No: MAU1721). 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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Copyright information

© 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 Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  4. 4.Department of Brain and Cognitive EngineeringKorea UniversitySeoulKorea

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