2DRLPP: Robust two-dimensional locality preserving projection with regularization

Chen, WJ; Li, CN; Shao, YH; Zhang, J; Deng, NY

2DRLPP: Robust two-dimensional locality preserving projection with regularization

Chen, WJ Li, CN Shao, YH Zhang, J Deng, NY

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Publication Type:: Journal Article
Citation:: Knowledge-Based Systems, 2019, 169 pp. 53 - 66
Issue Date:: 2019-04-01

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Field	Value	Language
dc.contributor.author	Chen, WJ	en_US
dc.contributor.author	Li, CN	en_US
dc.contributor.author	Shao, YH	en_US
dc.contributor.author	Zhang, J	en_US
dc.contributor.author	Deng, NY	en_US
dc.date.accessioned	2020-03-16T00:33:58Z
dc.date.available	2020-03-16T00:33:58Z
dc.date.issued	2019-04-01	en_US
dc.identifier.citation	Knowledge-Based Systems, 2019, 169 pp. 53 - 66	en_US
dc.identifier.issn	0950-7051	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139316
dc.description.abstract	© 2019 Elsevier B.V. The recently proposed two-dimensional locality preserving projection (2DLPP) is an excellent matrix-based dimensionality reduction method. However, the formulation of 2DLPP may encounter several drawbacks in real-world applications, such as sensitiveness to outliers, non-orthogonal projections, and singularity problem. To alleviate these issues, in this paper, we propose a novel robust two-dimensional locality preserving projection (2DRLPP) for noisy image recognition. The proposed 2DRLPP preserves the local manifold structure of two-dimensional image space under the robust L 1 -norm criterion. Different from the existing 2DLPP, our 2DRLPP enjoys several incomparable advantages: (i) 2DRLPP extracts projections from matrix-based image space via the L 1 -norm locality preserving criterion rather than the L 2 -norm one, and hence it is more robust to outliers. (ii) The orthogonality constraint is further imposed on 2DRLPP to ensure its orthogonal projections. (iii) The introduced regularization term can not only control the model complexity but also guarantee the stability of solution. (iv) A simple but efficient iterative algorithm is presented to solve the corresponding L 1 -norm minimization problem, whose convergence is guaranteed theoretically. Extensive experimental results on three noisy face image datasets confirm the feasibility and robustness of the proposed approach.	en_US
dc.relation.ispartof	Knowledge-Based Systems	en_US
dc.relation.isbasedon	10.1016/j.knosys.2019.01.022	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	2DRLPP: Robust two-dimensional locality preserving projection with regularization	en_US
dc.type	Journal Article
utslib.citation.volume	169	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	169	en_US

Abstract:

© 2019 Elsevier B.V. The recently proposed two-dimensional locality preserving projection (2DLPP) is an excellent matrix-based dimensionality reduction method. However, the formulation of 2DLPP may encounter several drawbacks in real-world applications, such as sensitiveness to outliers, non-orthogonal projections, and singularity problem. To alleviate these issues, in this paper, we propose a novel robust two-dimensional locality preserving projection (2DRLPP) for noisy image recognition. The proposed 2DRLPP preserves the local manifold structure of two-dimensional image space under the robust L 1 -norm criterion. Different from the existing 2DLPP, our 2DRLPP enjoys several incomparable advantages: (i) 2DRLPP extracts projections from matrix-based image space via the L 1 -norm locality preserving criterion rather than the L 2 -norm one, and hence it is more robust to outliers. (ii) The orthogonality constraint is further imposed on 2DRLPP to ensure its orthogonal projections. (iii) The introduced regularization term can not only control the model complexity but also guarantee the stability of solution. (iv) A simple but efficient iterative algorithm is presented to solve the corresponding L 1 -norm minimization problem, whose convergence is guaranteed theoretically. Extensive experimental results on three noisy face image datasets confirm the feasibility and robustness of the proposed approach.

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http://hdl.handle.net/10453/139316