| Spectral curvature clustering (SCC) G Chen, G Lerman International Journal of Computer Vision 81, 317-330, 2009 | 478 | 2009 |
| Hybrid linear modeling via local best-fit flats T Zhang, A Szlam, Y Wang, G Lerman International journal of computer vision 100 (3), 217-240, 2012 | 245 | 2012 |
| Median K-flats for hybrid linear modeling with many outliers T Zhang, A Szlam, G Lerman Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International …, 2009 | 216 | 2009 |
| Robust computation of linear models by convex relaxation G Lerman, M McCoy, JA Tropp, T Zhang Foundations of Computational Mathematics 15 (2), 363-410, 2015 | 209* | 2015 |
| A novel M-estimator for robust PCA T Zhang, G Lerman The Journal of Machine Learning Research 15 (1), 749-808, 2014 | 148 | 2014 |
| An overview of robust subspace recovery G Lerman, T Maunu Proceedings of the IEEE 106 (8), 1380-1410, 2018 | 141 | 2018 |
| Graph convolutional neural networks via scattering D Zou, G Lerman Applied and Computational Harmonic Analysis 49 (3), 1046-1074, 2020 | 129 | 2020 |
| Spectral clustering based on local PCA E Arias-Castro, G Lerman, T Zhang Journal of Machine Learning Research 18 (9), 1-57, 2017 | 129* | 2017 |
| Spectral clustering based on local linear approximations E Arias-Castro, G Chen, G Lerman Electronic Journal of Statistics 5, 1537-1587, 2011 | 119 | 2011 |
| Robust recovery of multiple subspaces by geometric lp minimization G Lerman, T Zhang Annals of Statistics 39 (5), 2686-2715, 2011 | 116 | 2011 |
| Foundations of a multi-way spectral clustering framework for hybrid linear modeling G Chen, G Lerman Foundations of Computational Mathematics 9, 517-558, 2009 | 90 | 2009 |
| Fast, robust and non-convex subspace recovery G Lerman, T Maunu Information and Inference: A Journal of the IMA 7 (2), 277–336, 2017 | 89 | 2017 |
| Robust Subspace Recovery Layer for Unsupervised Anomaly Detection CH Lai, D Zou, G Lerman Online proceedings of the Eighth International Conference on Learning …, 2020 | 82 | 2020 |
| A well-tempered landscape for non-convex robust subspace recovery T Maunu, T Zhang, G Lerman Journal of Machine Learning Research 20 (37), 1-59, 2019 | 72 | 2019 |
| Quantifying curvelike structures of measures by using L2 Jones quantities G Lerman Communications on Pure and Applied Mathematics 56 (9), 1294-1365, 2003 | 70 | 2003 |
| Analysis and algorithms for ℓp-based semi-supervised learning on graphs M Flores, J Calder, G Lerman Applied and Computational Harmonic Analysis 60, 77-122, 2022 | 64* | 2022 |
| Robust stochastic principal component analysis J Goes, T Zhang, R Arora, G Lerman Proceedings of the 17th International Conference on Artificial Intelligence …, 2014 | 60 | 2014 |
| Robust locally linear analysis with applications to image denoising and blind inpainting Y Wang, A Szlam, G Lerman SIAM Journal on Imaging Sciences 6 (1), 526-562, 2013 | 60 | 2013 |
| Defining functional distance using manifold embeddings of gene ontology annotations G Lerman, BE Shakhnovich Proceedings of the National Academy of Sciences 104 (27), 11334-11339, 2007 | 53 | 2007 |
| Randomized hybrid linear modeling by local best-fit flats T Zhang, A Szlam, Y Wang, G Lerman Proceedings of the IEEE Computer Society Conference on Computer Vision and …, 2010 | 50 | 2010 |
