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Robust Nonnegative Patch Alignment for Dimensionality Reduction
Xinge You1; Weihua Ou2; Chun Lung Philip Chen3; Qiang Li4; Ziqi Zhu1; Yuanyan Tang3
2015-11
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
ISSN2162-237X
Volume26Issue:11Pages:2760 - 2774
Abstract

Dimensionality reduction is an important method to analyze high-dimensional data and has many applications in pattern recognition and computer vision. In this paper, we propose a robust nonnegative patch alignment for dimensionality reduction, which includes a reconstruction error term and a whole alignment term. We use correntropy-induced metric to measure the reconstruction error, in which the weight is learned adaptively for each entry. For the whole alignment, we propose locality-preserving robust nonnegative patch alignment (LP-RNA) and sparsity-preserviing robust nonnegative patch alignment (SP-RNA), which are unsupervised and supervised, respectively. In the LP-RNA, we propose a locally sparse graph to encode the local geometric structure of the manifold embedded in high-dimensional space. In particular, we select large p-nearest neighbors for each sample, then obtain the sparse representation with respect to these neighbors. The sparse representation is used to build a graph, which simultaneously enjoys locality, sparseness, and robustness. In the SP-RNA, we simultaneously use local geometric structure and discriminative information, in which the sparse reconstruction coefficient is used to characterize the local geometric structure and weighted distance is used to measure the separability of different classes. For the induced nonconvex objective function, we formulate it into a weighted nonnegative matrix factorization based on half-quadratic optimization. We propose a multiplicative update rule to solve this function and show that the objective function converges to a local optimum. Several experimental results on synthetic and real data sets demonstrate that the learned representation is more discriminative and robust than most existing dimensionality reduction methods. 

KeywordCorrentropy-induced Metric (Cim) Dimensionality Reduction Locality Preserving (Lp) Robust Nonnegative Patch Alignment (Rnpa) Sparsity Preserving (Sp)
DOIhttps://doi.org/10.1109/TNNLS.2015.2393886
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000363242800012
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
The Source to ArticleScopus
Fulltext Access
Citation statistics
Cited Times [WOS]:20   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorXinge You; Weihua Ou; Chun Lung Philip Chen; Qiang Li; Ziqi Zhu; Yuanyan Tang
Affiliation1.School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
2.School of Mathematics and Computer Science, Guizhou Normal University, Guiyang 550001, China
3.Faculty of Science and Technology, University of Macau, Macau 853, China
4.Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology at Sydney, Sydney, NSW 2007, Australia
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Xinge You,Weihua Ou,Chun Lung Philip Chen,et al. Robust Nonnegative Patch Alignment for Dimensionality Reduction[J]. IEEE Transactions on Neural Networks and Learning Systems,2015,26(11):2760 - 2774.
APA Xinge You,Weihua Ou,Chun Lung Philip Chen,Qiang Li,Ziqi Zhu,&Yuanyan Tang.(2015).Robust Nonnegative Patch Alignment for Dimensionality Reduction.IEEE Transactions on Neural Networks and Learning Systems,26(11),2760 - 2774.
MLA Xinge You,et al."Robust Nonnegative Patch Alignment for Dimensionality Reduction".IEEE Transactions on Neural Networks and Learning Systems 26.11(2015):2760 - 2774.
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