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

Xiaofeng Zhu, Heung Il Suk, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)907-925
Number of pages19
JournalWorld Wide Web
Issue number2
Publication statusPublished - 2019 Mar 15

Bibliographical note

Publisher Copyright:
© 2018, Springer Science+Business Media, LLC, part of Springer Nature.


  • Alzheimer’s Disease (AD)
  • Feature selection
  • Subspace learning

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


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