Discriminative self-representation sparse regression for neuroimaging-based alzheimer's disease diagnosis.
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 framewo...
| Publicado en: | Brain Imaging & Behavior Vol. 13; no. 1; pp. 27 - 41 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Feb2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135233763&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135233763 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: Feb2019 vid: 13 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135233763 135233763 NLM28624881 135233763 10.1007/s11682-017-9731-x NLM28624881 135233763 ppf: 27 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Discriminative self-representation sparse regression for neuroimaging-based alzheimer's disease diagnosis. aug: au: Zhu, Xiaofeng Suk, Heung-Il Lee, Seong-Whan Shen, Dinggang affil: Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, Chapel Hill, USA sug: subj: Alzheimer's Disease Image Interpretation, Computer Assisted Methods Magnetic Resonance Imaging Methods Neuroradiography Methods Sensitivity and Specificity Human Aged Aged, 80 and Over Male Information Science Methods Female Middle Age Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales Aged: 65+ years Aged, 80 & over Middle Aged: 45-64 years Male Female ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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