Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/653786
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dc.contributor.authorYuwu Luen_US
dc.contributor.authorZhihui Laien_US
dc.contributor.authorYong Xuen_US
dc.contributor.authorXuelong Lien_US
dc.contributor.authorDavid Zhangen_US
dc.contributor.authorChun Yuanen_US
dc.date.accessioned2020-05-20T10:21:06Z-
dc.date.available2020-05-20T10:21:06Z-
dc.date.issued2016en_US
dc.identifier.issn2168-2267en_US
dc.identifier.issn2168-2275en_US
dc.identifier.other10.1109/TCYB.2015.2457611en_US
dc.identifier.urihttp://localhost/handle/Hannan/139639en_US
dc.identifier.urihttp://localhost/handle/Hannan/653786-
dc.description.abstractAs one of the most popular dimensionality reduction techniques, locality preserving projections (LPP) has been widely used in computer vision and pattern recognition. However, in practical applications, data is always corrupted by noises. For the corrupted data, samples from the same class may not be distributed in the nearest area, thus LPP may lose its effectiveness. In this paper, it is assumed that data is grossly corrupted and the noise matrix is sparse. Based on these assumptions, we propose a novel dimensionality reduction method, named low-rank preserving projections (LRPP) for image classification. LRPP learns a low-rank weight matrix by projecting the data on a low-dimensional subspace. We use the L<sub>21</sub> norm as a sparse constraint on the noise matrix and the nuclear norm as a low-rank constraint on the weight matrix. LRPP keeps the global structure of the data during the dimensionality reduction procedure and the learned low rank weight matrix can reduce the disturbance of noises in the data. LRPP can learn a robust subspace from the corrupted data. To verify the performance of LRPP in image dimensionality reduction and classification, we compare LRPP with the state-of-the-art dimensionality reduction methods. The experimental results show the effectiveness and the feasibility of the proposed method with encouraging results.en_US
dc.publisherIEEEen_US
dc.relation.haspart7182766.pdfen_US
dc.subjectlocality preserving projections (LPP)|Face recognition|image classification|low-rank representation (LRR)en_US
dc.titleLow-Rank Preserving Projectionsen_US
dc.typeArticleen_US
dc.journal.volume46en_US
dc.journal.issue8en_US
dc.journal.titleIEEE Transactions on Cyberneticsen_US
Appears in Collections:2016

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorYuwu Luen_US
dc.contributor.authorZhihui Laien_US
dc.contributor.authorYong Xuen_US
dc.contributor.authorXuelong Lien_US
dc.contributor.authorDavid Zhangen_US
dc.contributor.authorChun Yuanen_US
dc.date.accessioned2020-05-20T10:21:06Z-
dc.date.available2020-05-20T10:21:06Z-
dc.date.issued2016en_US
dc.identifier.issn2168-2267en_US
dc.identifier.issn2168-2275en_US
dc.identifier.other10.1109/TCYB.2015.2457611en_US
dc.identifier.urihttp://localhost/handle/Hannan/139639en_US
dc.identifier.urihttp://localhost/handle/Hannan/653786-
dc.description.abstractAs one of the most popular dimensionality reduction techniques, locality preserving projections (LPP) has been widely used in computer vision and pattern recognition. However, in practical applications, data is always corrupted by noises. For the corrupted data, samples from the same class may not be distributed in the nearest area, thus LPP may lose its effectiveness. In this paper, it is assumed that data is grossly corrupted and the noise matrix is sparse. Based on these assumptions, we propose a novel dimensionality reduction method, named low-rank preserving projections (LRPP) for image classification. LRPP learns a low-rank weight matrix by projecting the data on a low-dimensional subspace. We use the L<sub>21</sub> norm as a sparse constraint on the noise matrix and the nuclear norm as a low-rank constraint on the weight matrix. LRPP keeps the global structure of the data during the dimensionality reduction procedure and the learned low rank weight matrix can reduce the disturbance of noises in the data. LRPP can learn a robust subspace from the corrupted data. To verify the performance of LRPP in image dimensionality reduction and classification, we compare LRPP with the state-of-the-art dimensionality reduction methods. The experimental results show the effectiveness and the feasibility of the proposed method with encouraging results.en_US
dc.publisherIEEEen_US
dc.relation.haspart7182766.pdfen_US
dc.subjectlocality preserving projections (LPP)|Face recognition|image classification|low-rank representation (LRR)en_US
dc.titleLow-Rank Preserving Projectionsen_US
dc.typeArticleen_US
dc.journal.volume46en_US
dc.journal.issue8en_US
dc.journal.titleIEEE Transactions on Cyberneticsen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7182766.pdf3.24 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorYuwu Luen_US
dc.contributor.authorZhihui Laien_US
dc.contributor.authorYong Xuen_US
dc.contributor.authorXuelong Lien_US
dc.contributor.authorDavid Zhangen_US
dc.contributor.authorChun Yuanen_US
dc.date.accessioned2020-05-20T10:21:06Z-
dc.date.available2020-05-20T10:21:06Z-
dc.date.issued2016en_US
dc.identifier.issn2168-2267en_US
dc.identifier.issn2168-2275en_US
dc.identifier.other10.1109/TCYB.2015.2457611en_US
dc.identifier.urihttp://localhost/handle/Hannan/139639en_US
dc.identifier.urihttp://localhost/handle/Hannan/653786-
dc.description.abstractAs one of the most popular dimensionality reduction techniques, locality preserving projections (LPP) has been widely used in computer vision and pattern recognition. However, in practical applications, data is always corrupted by noises. For the corrupted data, samples from the same class may not be distributed in the nearest area, thus LPP may lose its effectiveness. In this paper, it is assumed that data is grossly corrupted and the noise matrix is sparse. Based on these assumptions, we propose a novel dimensionality reduction method, named low-rank preserving projections (LRPP) for image classification. LRPP learns a low-rank weight matrix by projecting the data on a low-dimensional subspace. We use the L<sub>21</sub> norm as a sparse constraint on the noise matrix and the nuclear norm as a low-rank constraint on the weight matrix. LRPP keeps the global structure of the data during the dimensionality reduction procedure and the learned low rank weight matrix can reduce the disturbance of noises in the data. LRPP can learn a robust subspace from the corrupted data. To verify the performance of LRPP in image dimensionality reduction and classification, we compare LRPP with the state-of-the-art dimensionality reduction methods. The experimental results show the effectiveness and the feasibility of the proposed method with encouraging results.en_US
dc.publisherIEEEen_US
dc.relation.haspart7182766.pdfen_US
dc.subjectlocality preserving projections (LPP)|Face recognition|image classification|low-rank representation (LRR)en_US
dc.titleLow-Rank Preserving Projectionsen_US
dc.typeArticleen_US
dc.journal.volume46en_US
dc.journal.issue8en_US
dc.journal.titleIEEE Transactions on Cyberneticsen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7182766.pdf3.24 MBAdobe PDFThumbnail
Preview File