Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/15161
Full metadata record
DC FieldValueLanguage
dc.contributor.authorZhu, Yanen
dc.date.accessioned2009-04-08T00:56:25Zen
dc.date.available2009-04-08T00:56:25Zen
dc.date.copyright2009en
dc.date.issued2009en
dc.identifier.citationZhu, Y. (2009). Further insights into subspace methods with applications in face recognition. Doctoral thesis, Nanyang Technological University, Singapore.en
dc.identifier.urihttps://hdl.handle.net/10356/15161en
dc.description.abstractSubspace methods such as Linear Discriminant Analysis (LDA) are efficient in dimension reduction and statistical feature extraction. They are widely applied to multi-class pattern classification problems, such as face recognition, which often involve high dimensional and large data set. In this thesis, we provide further insights into the subspace methods to resolve some prolonged issues. Firstly, we propose the Margin-Maximization Discriminant Analysis (MMDA) based on an additive-form of discriminant function, which can extract features that approximately maximize the average projected margin between the classes. Secondly, an analytical relevance measure of subspace feature vectors is derived and used to weigh the LDA features. This leads to a scheme called Relevance-Weighted Discriminant Analysis (RWDA). It completely eliminates the peaking phenomenon of LDA and also suggests a new insight into the root cause of overfitting for classifiers using distance metric. Finally, 2D subspace methods which represent images as 2D matrices are investigated, in order to tackle the computation intractability of large-scale pattern classification problems.en
dc.format.extent130 p.en
dc.language.isoenen
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Biometricsen
dc.titleFurther insights into subspace methods with applications in face recognitionen
dc.typeThesisen
dc.contributor.supervisorSung Ericen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.description.degreeDOCTOR OF PHILOSOPHY (EEE)en
dc.identifier.doi10.32657/10356/15161en
item.grantfulltextopen-
item.fulltextWith Fulltext-
Appears in Collections:EEE Theses
Files in This Item:
File Description SizeFormat 
ZhuYan2009.pdfMain report2.51 MBAdobe PDFThumbnail
View/Open

Page view(s) 50

346
Updated on Nov 26, 2021

Download(s) 20

196
Updated on Nov 26, 2021

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.