A novel relational regularization feature selection method for joint regression and classification in AD diagnosis.

Journal Article (Journal Article)

In this paper, we focus on joint regression and classification for Alzheimer's disease diagnosis and propose a new feature selection method by embedding the relational information inherent in the observations into a sparse multi-task learning framework. Specifically, the relational information includes three kinds of relationships (such as feature-feature relation, response-response relation, and sample-sample relation), for preserving three kinds of the similarity, such as for the features, the response variables, and the samples, respectively. To conduct feature selection, we first formulate the objective function by imposing these three relational characteristics along with an ℓ2,1-norm regularization term, and further propose a computationally efficient algorithm to optimize the proposed objective function. With the dimension-reduced data, we train two support vector regression models to predict the clinical scores of ADAS-Cog and MMSE, respectively, and also a support vector classification model to determine the clinical label. We conducted extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to validate the effectiveness of the proposed method. Our experimental results showed the efficacy of the proposed method in enhancing the performances of both clinical scores prediction and disease status identification, compared to the state-of-the-art methods.

Full Text

Duke Authors

Cited Authors

  • Zhu, X; Suk, H-I; Wang, L; Lee, S-W; Shen, D; Alzheimer’s Disease Neuroimaging Initiative,

Published Date

  • May 2017

Published In

Volume / Issue

  • 38 /

Start / End Page

  • 205 - 214

PubMed ID

  • 26674971

Pubmed Central ID

  • PMC4862945

Electronic International Standard Serial Number (EISSN)

  • 1361-8423

Digital Object Identifier (DOI)

  • 10.1016/j.media.2015.10.008


  • eng

Conference Location

  • Netherlands