Discriminative self-representation sparse regression for neuroimaging-based alzheimer's disease diagnosis
DC Field | Value | Language |
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dc.contributor.author | Zhu, Xiaofeng | - |
dc.contributor.author | Suk, Heung-Il | - |
dc.contributor.author | Lee, Seong-Whan | - |
dc.contributor.author | Shen, Dinggang | - |
dc.date.accessioned | 2021-09-01T20:10:17Z | - |
dc.date.available | 2021-09-01T20:10:17Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2019-02 | - |
dc.identifier.issn | 1931-7557 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/67839 | - |
dc.description.abstract | 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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.subject | MILD COGNITIVE IMPAIRMENT | - |
dc.subject | FEATURE-SELECTION | - |
dc.subject | CLASSIFICATION | - |
dc.subject | ATROPHY | - |
dc.subject | IMAGES | - |
dc.subject | MODEL | - |
dc.subject | LINE | - |
dc.title | Discriminative self-representation sparse regression for neuroimaging-based alzheimer's disease diagnosis | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Seong-Whan | - |
dc.contributor.affiliatedAuthor | Shen, Dinggang | - |
dc.identifier.doi | 10.1007/s11682-017-9731-x | - |
dc.identifier.scopusid | 2-s2.0-85020552403 | - |
dc.identifier.wosid | 000460795600003 | - |
dc.identifier.bibliographicCitation | BRAIN IMAGING AND BEHAVIOR, v.13, no.1, pp.27 - 40 | - |
dc.relation.isPartOf | BRAIN IMAGING AND BEHAVIOR | - |
dc.citation.title | BRAIN IMAGING AND BEHAVIOR | - |
dc.citation.volume | 13 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 27 | - |
dc.citation.endPage | 40 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Neurosciences & Neurology | - |
dc.relation.journalWebOfScienceCategory | Neuroimaging | - |
dc.subject.keywordPlus | MILD COGNITIVE IMPAIRMENT | - |
dc.subject.keywordPlus | FEATURE-SELECTION | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | ATROPHY | - |
dc.subject.keywordPlus | IMAGES | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | LINE | - |
dc.subject.keywordAuthor | Alzheimer&apos | - |
dc.subject.keywordAuthor | s disease (AD) | - |
dc.subject.keywordAuthor | Mild cognitive impairment (MCI) | - |
dc.subject.keywordAuthor | Feature selection | - |
dc.subject.keywordAuthor | Joint sparse learning | - |
dc.subject.keywordAuthor | Self-representation | - |
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