Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/204261
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dc.contributor.authorYuwu Luen_US
dc.contributor.authorChun Yuanen_US
dc.contributor.authorZhihui Laien_US
dc.contributor.authorXuelong Lien_US
dc.contributor.authorWai Keung Wongen_US
dc.contributor.authorDavid Zhangen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:51:44Z-
dc.date.available2020-04-06T07:51:44Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TMM.2017.2703130en_US
dc.identifier.urihttp://localhost/handle/Hannan/204261-
dc.description.abstractTwo-dimensional locality preserving projections (2DLPP) that use 2D image representation in preserving projection learning can preserve the intrinsic manifold structure and local information of data. However, 2DLPP is based on the Euclidean distance, which is sensitive to noise and outliers in data. In this paper, we propose a novel locality preserving projection method called nuclear norm-based two-dimensional locality preserving projections (NN-2DLPP). First, NN-2DLPP recovers the noisy data matrix through low-rank learning. Second, noise in data is removed and the learned clean data points are projected on a new subspace. Without the disturbance of noise, data points belonging to the same class are kept as close to each other as possible in the new projective subspace. Experimental results on six public image databases with face recognition, object classification, and handwritten digit recognition tasks demonstrated the effectiveness of the proposed method.en_US
dc.format.extent2391,en_US
dc.format.extent2403en_US
dc.publisherIEEEen_US
dc.relation.haspart7924357.pdfen_US
dc.titleNuclear Norm-Based 2DLPP for Image Classificationen_US
dc.typeArticleen_US
dc.journal.volume19en_US
dc.journal.issue11en_US
Appears in Collections:2017

Files in This Item:
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7924357.pdf1.42 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorYuwu Luen_US
dc.contributor.authorChun Yuanen_US
dc.contributor.authorZhihui Laien_US
dc.contributor.authorXuelong Lien_US
dc.contributor.authorWai Keung Wongen_US
dc.contributor.authorDavid Zhangen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:51:44Z-
dc.date.available2020-04-06T07:51:44Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TMM.2017.2703130en_US
dc.identifier.urihttp://localhost/handle/Hannan/204261-
dc.description.abstractTwo-dimensional locality preserving projections (2DLPP) that use 2D image representation in preserving projection learning can preserve the intrinsic manifold structure and local information of data. However, 2DLPP is based on the Euclidean distance, which is sensitive to noise and outliers in data. In this paper, we propose a novel locality preserving projection method called nuclear norm-based two-dimensional locality preserving projections (NN-2DLPP). First, NN-2DLPP recovers the noisy data matrix through low-rank learning. Second, noise in data is removed and the learned clean data points are projected on a new subspace. Without the disturbance of noise, data points belonging to the same class are kept as close to each other as possible in the new projective subspace. Experimental results on six public image databases with face recognition, object classification, and handwritten digit recognition tasks demonstrated the effectiveness of the proposed method.en_US
dc.format.extent2391,en_US
dc.format.extent2403en_US
dc.publisherIEEEen_US
dc.relation.haspart7924357.pdfen_US
dc.titleNuclear Norm-Based 2DLPP for Image Classificationen_US
dc.typeArticleen_US
dc.journal.volume19en_US
dc.journal.issue11en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7924357.pdf1.42 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorYuwu Luen_US
dc.contributor.authorChun Yuanen_US
dc.contributor.authorZhihui Laien_US
dc.contributor.authorXuelong Lien_US
dc.contributor.authorWai Keung Wongen_US
dc.contributor.authorDavid Zhangen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:51:44Z-
dc.date.available2020-04-06T07:51:44Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TMM.2017.2703130en_US
dc.identifier.urihttp://localhost/handle/Hannan/204261-
dc.description.abstractTwo-dimensional locality preserving projections (2DLPP) that use 2D image representation in preserving projection learning can preserve the intrinsic manifold structure and local information of data. However, 2DLPP is based on the Euclidean distance, which is sensitive to noise and outliers in data. In this paper, we propose a novel locality preserving projection method called nuclear norm-based two-dimensional locality preserving projections (NN-2DLPP). First, NN-2DLPP recovers the noisy data matrix through low-rank learning. Second, noise in data is removed and the learned clean data points are projected on a new subspace. Without the disturbance of noise, data points belonging to the same class are kept as close to each other as possible in the new projective subspace. Experimental results on six public image databases with face recognition, object classification, and handwritten digit recognition tasks demonstrated the effectiveness of the proposed method.en_US
dc.format.extent2391,en_US
dc.format.extent2403en_US
dc.publisherIEEEen_US
dc.relation.haspart7924357.pdfen_US
dc.titleNuclear Norm-Based 2DLPP for Image Classificationen_US
dc.typeArticleen_US
dc.journal.volume19en_US
dc.journal.issue11en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7924357.pdf1.42 MBAdobe PDF