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Med Image Anal. 2017 May;38:205-214. doi: 10.1016/j.media.2015.10.008. Epub 2015 Nov 10.

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

Author information

1
Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, USA.
2
Department of Brain and Cognitive Engineering, Korea University, Republic of Korea.
3
Department of Brain and Cognitive Engineering, Korea University, Republic of Korea. Electronic address: sw.lee@korea.ac.kr.
4
Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, USA; Department of Brain and Cognitive Engineering, Korea University, Republic of Korea. Electronic address: dgshen@med.unc.edu.

Abstract

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.

KEYWORDS:

Alzheimer’s disease; Feature selection; MCI conversion; Manifold learning; Sparse coding

PMID:
26674971
PMCID:
PMC4862945
DOI:
10.1016/j.media.2015.10.008
[Indexed for MEDLINE]
Free PMC Article

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