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·462· 智能系统学报 第14卷 染,有效减少了样本中的污染对实验结果造成的 [J].Neurocomputing,2015,168:70-80. 负面影响。因此SWLRR算法样本在有无遮挡的 [12]CANDES E J,LI Xiaodong,MA Yi,et al.Robust prin- 情况下都有着较为突出的性能。在AR、Extended cipal component analysis[J].Journal of the ACM,2011, Yale B、CMU PIE数据库上的实验验证了SWLRR 58(311. 算法在不同情况下的有效性和对噪声的鲁棒性。 [13]LIU Guangcan,LIN Zhouchen,YAN Shuicheng,et al. 下一步将尝试将本文思想与其他解决样本中存在 Robust recovery of subspace structures by low-rank rep- 污染的算法结合,进一步提升算法性能。 resentation[J].IEEE transactions on pattern analysis and machine intelligence,2013,35(1):171-184. 参考文献: [14]MA Long,WANG Chunheng,XIAO Baihua,et al.Sparse representation for face recognition based on discriminat- [1]TURK M,PENTLAND A.Eigenfaces for recognition[J]. ive low-rank dictionary learning[C]//Proceedings of 2012 Journal of cognitive neuroscience,1991,3(1):71-86. 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Improved combination of LBP and sparse representation based classification (SRC) for face recognition[C]//Proceedings of 2011 IEEE ICME. Barcelona, Spain, 2011: 1–6. [6] CHEN C F, WEI C P, WANG Y C F. Low-rank matrix re￾covery with structural incoherence for robust face recogni￾tion[C]//Proceedings of 2012 IEEE Conference on Com￾puter Vision and Pattern Recognition. Providence, USA, 2012: 2618–2625. [7] YANG Meng, ZHANG Lei, YANG Jian, et al. Regular￾ized robust coding for face recognition[J]. IEEE transac￾tions on image processing, 2013, 22(5): 1753–1766. [8] ZHANG Lei, YANG Meng, FENG Xiangchu. Sparse rep￾resentation or collaborative representation: which helps face recognition?[C]//Proceedings of 2011 ICCV. Bar￾celona, Spain, 2011: 471–478. [9] DENG Weihong, HU Jiani, GUO Jun. Extended SRC: un￾dersampled face recognition via intraclass variant diction￾ary[J]. IEEE transactions on pattern analysis and machine intelligence, 2012, 34(9): 1864–1870. [10] YANG Meng, ZHU Pengfei, LIU Feng, et al. Joint repres￾entation and pattern learning for robust face recognition [11] [J]. Neurocomputing, 2015, 168: 70–80. CANDÈS E J, LI Xiaodong, MA Yi, et al. Robust prin￾cipal component analysis[J]. Journal of the ACM, 2011, 58(3): 11. [12] LIU Guangcan, LIN Zhouchen, YAN Shuicheng, et al. Robust recovery of subspace structures by low-rank rep￾resentation[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(1): 171–184. [13] MA Long, WANG Chunheng, XIAO Baihua, et al. Sparse representation for face recognition based on discriminat￾ive low-rank dictionary learning[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recog￾nition. Providence, USA, 2012: 2586–2593. [14] ZHANG Yangmuzi, JIANG Zhuolin, DAVIS L S. Learn￾ing structured low-rank representations for image classi￾fication[C]//Proceeding of 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA, 2013: 676–683. [15] NGUYEN H, YANG Wankou, SHENG Biyun, et al. Dis￾criminative low-rank dictionary learning for face recogni￾tion[J]. Neurocomputing, 2016, 173(3): 541–551. [16] CHANG Heyou, ZHENG Hao. Weighted discriminative dictionary learning based on lowrank representation [J]. Journal of physics: conference series, 2017, 90: 012009. [17] ZHANG Zheng, XU Yong, SHAO Ling, et al. Discrimin￾ative block-diagonal representation learning for image re￾cognition[J]. IEEE transactions on neural networks and learning systems, 2018, 29(7): 3111–3125. [18] CHEN Jie, YI Zhang. Sparse representation for face re￾cognition by discriminative low-rank matrix recovery[J]. Journal of visual communication and image representa￾tion, 2014, 25(5): 763–773. [19] COSTEIRA J P, KANADE T. A multibody factorization method for independently moving objects[J]. Internation￾al journal of computer vision, 1998, 29(3): 159–179. [20] ZHUANG Liansheng, GAO Haoyuan, LIN Zhouchen, et al. Non-negative low rank and sparse graph for semi-su￾pervised learning[C]//Proceedings of 2012 IEEE Confer￾ence on Computer Vision and Pattern Recognition. Providence, USA, 2012: 2328–2335. [21] LIN Zhouchen, LIU Risheng, SU Zhixun. Linearized al￾ternating direction method with adaptive penalty for low￾rank representation[J]. Advance in neural information processing systems, 2011: 612-620. [22] LIN Zhouchen, CHEN Minming, MA Yi. The augmented lagrange multiplier method for exact recovery of corrup￾ted low-rank matrices[R]. Urbana-Champaign: Uni￾versity of Illinois at Urbana-Champaign, 2009. [23] ·462· 智 能 系 统 学 报 第 14 卷
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