G Liu, Z, Lin S Yan, J Sun, Y Yu, Y. MaRobust recovery of subspace structures by low-rank representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1): 171–184 https://doi.org/10.1109/TPAMI.2012.88
2
H Zhang, C Gong, J, Qian B Zhang, C Xu, J Yang. Efficient recovery of low-rank matrix via double nonconvex nonsmooth rank minimization. IEEE Transactions on Neural Networks, 2019, 30(10): 2916–2925 https://doi.org/10.1109/TNNLS.2019.2900572
3
Y Wang, V Morariu, L S Davis. 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 https://doi.org/10.1109/WACV.2015.78
4
Z Lei, M Pietikainen, S Z Li. Learning discriminant face descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(2): 289–302 https://doi.org/10.1109/TPAMI.2013.112
5
X Shi, Z Guo, F, Nie L Yang, J You, D Tao. Two-dimensional whitening reconstruction for enhancing robustness of principal component analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 38(10): 2130–2136 https://doi.org/10.1109/TPAMI.2015.2501810
6
X Zhu, Z Ghahramani, J Lafferty. Semi-supervised learning using gaussian fields and harmonic functions. In: Proceedings of the 20th International Conference on Machine Learning. 2003, 912–919
7
G Liu, S Yan. Latent low-rank representation for subspace segmentation and feature extraction. In: Proceedings of the IEEE International Conference on Computer Vision. 2011, 1615–1622 https://doi.org/10.1109/ICCV.2011.6126422
8
L, Fei Y Xu, X Fang, J Yang. Low rank representation with adaptive distance penalty for semi-supervised subspace classification. Pattern Recognition, 2017, 67: 252–262 https://doi.org/10.1016/j.patcog.2017.02.017
