Computer Science ›› 2015, Vol. 42 ›› Issue (3): 296-300.doi: 10.11896/j.issn.1002-137X.2015.03.061

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Feature Extraction Based on Low Rank Representation Linear Preserving Projections

YANG Guo-liang, XIE Nai-jun, YU Jia-wei and LIANG Li-ming   

  • Online:2018-11-14 Published:2018-11-14

Abstract: For preserving the low rank properties the same,we proposed an algorithm,called linear preserving projection based on low rank representations (LLRLPP),to reduce the dimension of data.It can preserve the low rank properties of the original data space in the resulting low dimensional embedding subspace and correctly learn the low-dimensional subspace.Through constructing two different low rank representation model,the low rank weights of representing different structural characteristics are revealed.Then the low-dimensional subspace of the original high-dimensional data is obtained by preserving such low rank weight relationship.The effectiveness of the proposed method is verified on two face databases(ORL,Yale) with the traditional algorithms.

Key words: Low rank representation,Lowrank weight,Linear preserving projections,Feature extraction

[1] Zou H,Hastie T,Tibshirani R.Sparse principal component anal-ysis[J].Journal of computational and graphical statistics,2006,15(2):265-286
[2] Martínez A M,Kak A C.Pca versus lda[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(2):228-233
[3] Roweis S T,Saul L K.Nonlinear dimensionality reduction by lo-cally linear embedding[J].Science,2000,290(5500):2323-2326
[4] Borg I,Groenen P J F.Modern multidimensional scaling:Theory and applications[M].Springer,2005
[5] Belkin M,Niyogi P.Laplacian eigenmaps and spectral techniques for embedding and clustering[C]∥NIPS.2001,14:585-591
[6] Tenenbaum J B,De Silva V,Langford J C.A global geometric framework for nonlinear dimensionality reduction[J].Science,2000,290(5500):2319-2323
[7] He X,Niyogi P.Locality preserving projections[C]∥NIPS.2003,16:234-241
[8] He X,Cai D,Yan S,et al.Neighborhood preserving embedding[C]∥Tenth IEEE International Conference on Computer Vision,2005(ICCV 2005).IEEE,2005,2:1208-1213
[9] Qiao L,Chen S,Tan X.Sparsity preserving projections with applications to face recognition[J].Pattern Recognition,2010,43(1):331-341
[10] Favaro P,Vidal R,Ravichandran A.A closed form solution to robust subspace estimation and clustering[C]∥IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2011.IEEE,2011:1801-1807
[11] Liu G,Lin Z,Yu Y.Robust subspace segmentation by low-rank representation[C]∥Proceedings of the 27th International Conference on Machine Learning (ICML-10).2010:663-670
[12] Candès E J,Li X,Ma Y,et al.Robust principal component analysis?[J].Journal of the ACM (JACM),2011,58(3):11
[13] Liu G,Lin Z,Yan S,et al.Robust recovery of subspace struc-tures by low-rank representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(1):171-184
[14] Liu G,Yan S.Latent low-rank representation for subspace segmentation and feature extraction[C]∥IEEE International Conference on Computer Vision (ICCV),2011.IEEE,2011:1615-1622
[15] Lin Z,Chen M,Ma Y.The augmented lagrange multiplier methodfor exact recovery of corrupted low-rank matrices[J].arXiv preprint arXiv:1009.5055,2010
[16] Vidal R,Favaro P.Low rank subspace clustering (LRSC)[J].Pattern Recognition Letters,2014,43(1):47-61
[17] Candes E J,Wakin M B,Boyd S P.Enhancing sparsity by re-weighted l1 minimization[J].Journal of Fourier analysis and applications,2008,14(5/6):877-905
[18] Lin Z,Chen M,Ma Y.The augmented lagrange multiplier methodfor exact recovery of corrupted low-rank matrices[J].arXiv preprint arXiv:1009.5055,2010
[19] Peng Y,Suo J,Dai Q,et al.ReweightedLow-Rank Matrix Recovery and its Application in Image Restortion[J].IEEE Transactions on Cybernetics,2014,44(12):2418-2430

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