Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/653786
Title: Low-Rank Preserving Projections
Authors: Yuwu Lu;Zhihui Lai;Yong Xu;Xuelong Li;David Zhang;Chun Yuan
subject: locality preserving projections (LPP)|Face recognition|image classification|low-rank representation (LRR)
Year: 2016
Publisher: IEEE
Abstract: As 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.
URI: http://localhost/handle/Hannan/139639
http://localhost/handle/Hannan/653786
ISSN: 2168-2267
2168-2275
volume: 46
issue: 8
Appears in Collections:2016

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Title: Low-Rank Preserving Projections
Authors: Yuwu Lu;Zhihui Lai;Yong Xu;Xuelong Li;David Zhang;Chun Yuan
subject: locality preserving projections (LPP)|Face recognition|image classification|low-rank representation (LRR)
Year: 2016
Publisher: IEEE
Abstract: As 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.
URI: http://localhost/handle/Hannan/139639
http://localhost/handle/Hannan/653786
ISSN: 2168-2267
2168-2275
volume: 46
issue: 8
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7182766.pdf3.24 MBAdobe PDFThumbnail
Preview File
Title: Low-Rank Preserving Projections
Authors: Yuwu Lu;Zhihui Lai;Yong Xu;Xuelong Li;David Zhang;Chun Yuan
subject: locality preserving projections (LPP)|Face recognition|image classification|low-rank representation (LRR)
Year: 2016
Publisher: IEEE
Abstract: As 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.
URI: http://localhost/handle/Hannan/139639
http://localhost/handle/Hannan/653786
ISSN: 2168-2267
2168-2275
volume: 46
issue: 8
Appears in Collections:2016

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