spectral techniques for embed- ding and clustering. In Advances in neural information processing systems, pages 585–591, 2002. [2] Emmanuel J Cand` es, Xiaodong Li, Yi Ma, and John Wright. Robust principal component analysis? Journal of the ACM (JACM), 58(3):11, 2011. [3] Chun-Mei Feng, Ying-Lian Gao, Jin-Xing Liu, Juan Wang, Dong-Qin Wang, and Chang- Gang Wen. Joint-norm constraint and graph-laplacian pca method for feature extraction. BioMed research international, 2017, 2017. [4] Xiaofei He and Partha Niyogi. Locality preserving projections. In Advances in neural information processing systems, pages 153–160, 2004. [5] Bo Jiang, Chris Ding, Bio Luo, and Jin Tang. Graph-laplacian pca: Closed-form solution and robustness. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3492–3498, 2013. [6] Taisong Jin, Jun Yu, Jane You, Kun Zeng, Cuihua Li, and Zhengtao Yu. Low-rank matrix factorization with multiple hypergraph regularizer. Pattern Recognition, 48(3):1011–1022, 2015. [7] Nauman Shahid, Vassilis Kalofolias, Xavier Bresson, Michael Bronstein, and Pierre Van- dergheynst. Robust principal component analysis on graphs. In Proceedings of the IEEE International Conference on Computer Vision, pages 2812–2820, 2015. [8] Nauman Shahid, Nathanael Perraudin, Vassilis Kalofolias, Gilles Puy, and Pierre Van- dergheynst. Fast robust pca on graphs. IEEE Journal of Selected Topics in Signal Pro- cessing, 10(4):740–756, 2016. [9] Yanning Shen, Panagiotis A Traganitis, and Georgios B Giannakis. Nonlinear dimension- ality reduction on graphs. In Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017 IEEE 7th International Workshop on, pages 1–5. IEEE, 2017. [10] Liang Tao, Horace HS Ip, Yinglin Wang, and Xin Shu. Low rank approximation with sparse integration of multiple manifolds for data representation. Applied Intelligence, 42(3):430– 446, 2015. 13