Completed sample correlations and feature dependency-based unsupervised feature selection

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dc.citation.issue10
dc.citation.volume82
dc.contributor.authorLiu T
dc.contributor.authorHu R
dc.contributor.authorZhu Y
dc.date.accessioned2023-11-17T01:13:30Z
dc.date.accessioned2023-11-20T01:37:30Z
dc.date.available2022-10-03
dc.date.available2023-11-17T01:13:30Z
dc.date.available2023-11-20T01:37:30Z
dc.date.issued2023-04
dc.description.abstractSample correlations and feature relations are two pieces of information that are needed to be considered in the unsupervised feature selection, as labels are missing to guide model construction. Thus, we design a novel unsupervised feature selection scheme, in this paper, via considering the completed sample correlations and feature dependencies in a unified framework. Specifically, self-representation dependencies and graph construction are conducted to preserve and select the important neighbors for each sample in a comprehensive way. Besides, mutual information and sparse learning are designed to consider the correlations between features and to remove the informative features, respectively. Moreover, various constraints are constructed to automatically obtain the number of important neighbors and to conduct graph partition for the clustering task. Finally, we test the proposed method and verify the effectiveness and the robustness on eight data sets, comparing with nine state-of-the-art approaches with regard to three evaluation metrics for the clustering task.
dc.description.confidentialfalse
dc.edition.editionApril 2023
dc.format.pagination15305-15326
dc.identifier.citationLiu T, Hu R, Zhu Y. (2023). Completed sample correlations and feature dependency-based unsupervised feature selection. Multimedia Tools and Applications. 82. 10. (pp. 15305-15326).
dc.identifier.doi10.1007/s11042-022-13903-y
dc.identifier.eissn1573-7721
dc.identifier.elements-typejournal-article
dc.identifier.issn1380-7501
dc.identifier.piis11042-022-13903-y
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/69107
dc.languageEnglish
dc.publisherSpringer Science+Business Media, LLC
dc.publisher.urihttps://link.springer.com/article/10.1007/s11042-022-13903-y
dc.relation.isPartOfMultimedia Tools and Applications
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectUnsupervised learning
dc.subjectSample correlation
dc.subjectUnsupervised feature selection
dc.subjectGraph learning
dc.subjectSelf-representation
dc.subjectMutual information
dc.subjectSparse learning
dc.titleCompleted sample correlations and feature dependency-based unsupervised feature selection
dc.typeJournal article
pubs.elements-id457377
pubs.organisational-groupOther
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