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References
[1] Mikhail Belkin and Partha Niyogi. Laplacian eigenmaps and 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.
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