Clustered Discriminant Regression for High-Dimensional Data Feature Extraction and Its Applications in Healthcare and Additive Manufacturing

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dc.contributor.authorShen, Boen
dc.contributor.authorXie, Weijunen
dc.contributor.authorJames Kong, Zhenyuen
dc.date.accessioned2021-12-10T21:07:05Zen
dc.date.available2021-12-10T21:07:05Zen
dc.date.issued2021-10-01en
dc.date.updated2021-12-10T21:07:03Zen
dc.description.abstractThe recent increase in applications of high-dimensional data poses a severe challenge to data analytics, such as supervised classification, particularly for online applications. To tackle this challenge, efficient and effective methods for feature extraction are critical to the performance of classification analysis. The objective of this work is to develop a new supervised feature extraction method for high-dimensional data. It is achieved by developing a clustered discriminant regression (CDR) to extract informative and discriminant features for high-dimensional data. In CDR, the variables are clustered into different groups or subspaces, within which feature extraction is performed separately. The CDR algorithm, which is a greedy approach, is implemented to obtain the solution toward optimal feature extraction. One numerical study is performed to demonstrate the performance of the proposed method for variable selection. Three case studies using healthcare and additive manufacturing data sets are accomplished to demonstrate the classification performance of the proposed methods for real-world applications. The results clearly show that the proposed method is superior over the existing method for high-dimensional data feature extraction. Note to Practitioners - This article forwards a new supervised feature extraction method termed clustered discriminant regression. This method is highly effective for classification analysis of high-dimensional data, such as images or videos, where the number of variables is much larger than the number of samples. In our case studies on healthcare and additive manufacturing, the performance of classification analysis based on our method is superior over the existing feature extraction methods, which is confirmed by using various popular classification algorithms. For image classification, our method with elaborately selected classification algorithms can outperform a convolutional neural network. In addition, the computation efficiency of the proposed method is also promising, which enables its online applications, such as advanced manufacturing process monitoring and control.en
dc.description.versionAccepted versionen
dc.format.extentPages 1998-2010en
dc.format.extent13 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/TASE.2020.3029028en
dc.identifier.eissn1558-3783en
dc.identifier.issn1545-5955en
dc.identifier.issue4en
dc.identifier.orcidXie, Weijun [0000-0001-5157-1194]en
dc.identifier.urihttp://hdl.handle.net/10919/106934en
dc.identifier.volume18en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000704116700042&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectTechnologyen
dc.subjectAutomation & Control Systemsen
dc.subjectFeature extractionen
dc.subjectMedical servicesen
dc.subjectThree-dimensional printingen
dc.subjectData miningen
dc.subjectClustering algorithmsen
dc.subjectMonitoringen
dc.subjectInput variablesen
dc.subjectAdditive manufacturing (AM)en
dc.subjectclassification analysisen
dc.subjectclusteringen
dc.subjectdiscriminant regression (DR)en
dc.subjectgreedy algorithmen
dc.subjecthealthcareen
dc.subjectvariable selectionen
dc.subjectVARIABLE SELECTIONen
dc.subjectSHRINKAGEen
dc.subjectALGORITHMSen
dc.subjectSTRATEGYen
dc.subjectIndustrial Engineering & Automationen
dc.subject0906 Electrical and Electronic Engineeringen
dc.subject0910 Manufacturing Engineeringen
dc.subject0913 Mechanical Engineeringen
dc.titleClustered Discriminant Regression for High-Dimensional Data Feature Extraction and Its Applications in Healthcare and Additive Manufacturingen
dc.title.serialIEEE Transactions on Automation Science and Engineeringen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Industrial and Systems Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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