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

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish
JournalWorld Wide Web
DOIs
Publication statusAccepted/In press - 2018 Jan 1

Keywords

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

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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