Low-rank dimensionality reduction for multi-modality neurodegenerative disease identification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhu, Xiaofeng | - |
dc.contributor.author | Suk, Heung-Il | - |
dc.contributor.author | Shen, Dinggang | - |
dc.date.accessioned | 2021-09-01T18:05:53Z | - |
dc.date.available | 2021-09-01T18:05:53Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2019-03 | - |
dc.identifier.issn | 1386-145X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/67123 | - |
dc.description.abstract | In this paper, we propose a novel dimensionality reduction method of taking the advantages of the variability, sparsity, and low-rankness of neuroimaging data for Alzheimer's Disease (AD) classification. We first take the variability of neuroimaging data into account by partitioning them into sub-classes by means of clustering, which thus captures the underlying multi-peak distributional characteristics in neuroimaging data. We then iteratively conduct Low-Rank Dimensionality Reduction (LRDR) and orthogonal rotation in a sparse linear regression framework, in order to find the low-dimensional structure of high-dimensional data. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset showed that our proposed model helped enhance the performances of AD classification, outperforming the state-of-the-art methods. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.subject | MILD COGNITIVE IMPAIRMENT | - |
dc.subject | FEATURE-SELECTION | - |
dc.subject | ALZHEIMERS-DISEASE | - |
dc.subject | CLASSIFICATION | - |
dc.subject | PREDICTION | - |
dc.subject | REGRESSION | - |
dc.subject | CONVERSION | - |
dc.subject | ATROPHY | - |
dc.subject | FUSION | - |
dc.subject | SIZE | - |
dc.title | Low-rank dimensionality reduction for multi-modality neurodegenerative disease identification | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Suk, Heung-Il | - |
dc.contributor.affiliatedAuthor | Shen, Dinggang | - |
dc.identifier.doi | 10.1007/s11280-018-0645-3 | - |
dc.identifier.scopusid | 2-s2.0-85056463714 | - |
dc.identifier.wosid | 000462231500026 | - |
dc.identifier.bibliographicCitation | WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS, v.22, no.2, pp.907 - 925 | - |
dc.relation.isPartOf | WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS | - |
dc.citation.title | WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS | - |
dc.citation.volume | 22 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 907 | - |
dc.citation.endPage | 925 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.subject.keywordPlus | MILD COGNITIVE IMPAIRMENT | - |
dc.subject.keywordPlus | FEATURE-SELECTION | - |
dc.subject.keywordPlus | ALZHEIMERS-DISEASE | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | REGRESSION | - |
dc.subject.keywordPlus | CONVERSION | - |
dc.subject.keywordPlus | ATROPHY | - |
dc.subject.keywordPlus | FUSION | - |
dc.subject.keywordPlus | SIZE | - |
dc.subject.keywordAuthor | Alzheimer&apos | - |
dc.subject.keywordAuthor | s Disease (AD) | - |
dc.subject.keywordAuthor | Feature selection | - |
dc.subject.keywordAuthor | Subspace learning | - |
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