Discriminative self-representation sparse regression for neuroimaging-based alzheimer's disease diagnosis.

Journal Article (Journal Article)

In this paper, we propose a novel feature selection method by jointly considering (1) 'task-specific' relations between response variables (e.g., clinical labels in this work) and neuroimaging features and (2) 'self-representation' relations among neuroimaging features in a sparse regression framework. Specifically, the task-specific relation is devised to learn the relative importance of features for representation of response variables by a linear combination of the input features in a supervised manner, while the self-representation relation is used to take into account the inherent information among neuroimaging features such that any feature can be represented by a weighted sum of the other features, regardless of the label information, in an unsupervised manner. By integrating these two different relations along with a group sparsity constraint, we formulate a new sparse linear regression model for class-discriminative feature selection. The selected features are used to train a support vector machine for classification. To validate the effectiveness of the proposed method, we conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset; experimental results showed superiority of the proposed method over the state-of-the-art methods considered in this work.

Full Text

Duke Authors

Cited Authors

  • Zhu, X; Suk, H-I; Lee, S-W; Shen, D

Published Date

  • February 2019

Published In

Volume / Issue

  • 13 / 1

Start / End Page

  • 27 - 40

PubMed ID

  • 28624881

Pubmed Central ID

  • PMC5811409

Electronic International Standard Serial Number (EISSN)

  • 1931-7565

Digital Object Identifier (DOI)

  • 10.1007/s11682-017-9731-x


  • eng

Conference Location

  • United States