A Novel Multi-relation Regularization Method for Regression and Classification in AD Diagnosis

  • Xiaofeng Zhu
  • Heung-Il Suk
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)


In this paper, we consider the joint regression and classification in Alzheimer’s disease diagnosis and propose a novel multi-relation regularization method that exploits the relational information inherent in the observations and then combines it with an ℓ2,1-norm within a least square regression framework for feature selection. Specifically, we use three kinds of relationships: feature-feature relation, response-response relation, and sample-sample relation. By imposing these three relational characteristics along with the ℓ2,1-norm on the weight coefficients, we formulate a new objective function. After feature selection based on the optimal weight coefficients, we train two support vector regression models to predict the clinical scores of Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE), respectively, and a support vector classification model to identify the clinical label. We conducted clinical score prediction and disease status identification jointly on the Alzheimer’s Disease Neuroimaging Initiative dataset. The experimental results showed that the proposed regularization method outperforms the state-of-the-art methods, in the metrics of correlation coefficient and root mean squared error in regression and classification accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve in classification.


Alzheimer’s disease feature selection sparse coding manifold learning MCI conversion 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xiaofeng Zhu
    • 1
  • Heung-Il Suk
    • 1
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillUSA

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