Low-rank representation based robust face recognition by two-dimensional whitening reconstruction

References

[1] Liu G, Lin Z,Yan S, Sun J, Yu Y, Ma Y.Robust recovery of subspace structures by low-rank representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1): 171–184

[2] Zhang H, Gong C, Qian J,Zhang B, Xu C, Yang J. Efficient recovery of low-rank matrix via double nonconvex nonsmooth rank minimization. IEEE Transactions on Neural Networks, 2019, 30(10): 2916–2925

[3] Wang Y, Morariu V, Davis L S. Unsupervised feature extraction inspired by latent low-rank representation. In: Proceedings of the 2005 IEEE Winter Conference on Applications of Computer Vision. 2015, 542–549

[4] Lei Z, Pietikainen M, Li S Z. Learning discriminant face descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(2): 289–302

[5] Shi X, Guo Z, NieF, Yang L, You J, Tao D. Two-dimensional whitening reconstruction for enhancing robustness of principal component analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 38(10): 2130–2136

[6] Zhu X, Ghahramani Z, Lafferty J. Semi-supervised learning using gaussian fields and harmonic functions. In: Proceedings of the 20th International Conference on Machine Learning. 2003, 912–919

[7] Liu G, Yan S. Latent low-rank representation for subspace segmentation and feature extraction. In: Proceedings of the IEEE International Conference on Computer Vision. 2011, 1615–1622

[8] Fei L,Xu Y, Fang X, Yang J. Low rank representation with adaptive distance penalty for semi-supervised subspace classification. Pattern Recognition, 2017, 67: 252–262

[9] Wen J, Zhang B, Xu Y,Yang J, Han N. Adaptive weighted nonnegative low-rank representation. Pattern Recognition, 2018, 81: 326–340

RIGHTS & PERMISSIONS

2020 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature