Spectral curvature clustering (SCC) G Chen, G Lerman International Journal of Computer Vision 81, 317-330, 2009 | 467 | 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 | 239 | 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 | 210 | 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 | 206* | 2015 |
A novel M-estimator for robust PCA T Zhang, G Lerman The Journal of Machine Learning Research 15 (1), 749-808, 2014 | 144 | 2014 |
An overview of robust subspace recovery G Lerman, T Maunu Proceedings of the IEEE 106 (8), 1380-1410, 2018 | 136 | 2018 |
Spectral clustering based on local PCA E Arias-Castro, G Lerman, T Zhang Journal of Machine Learning Research 18 (9), 1-57, 2017 | 128* | 2017 |
Robust recovery of multiple subspaces by geometric lp minimization G Lerman, T Zhang Annals of Statistics 39 (5), 2686-2715, 2011 | 117 | 2011 |
Spectral clustering based on local linear approximations E Arias-Castro, G Chen, G Lerman Electronic Journal of Statistics 5, 1537-1587, 2011 | 115 | 2011 |
Graph convolutional neural networks via scattering D Zou, G Lerman Applied and Computational Harmonic Analysis 49 (3), 1046-1074, 2020 | 111 | 2020 |
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 |
Foundations of a multi-way spectral clustering framework for hybrid linear modeling G Chen, G Lerman Foundations of Computational Mathematics 9, 517-558, 2009 | 87 | 2009 |
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 |
Robust Subspace Recovery Layer for Unsupervised Anomaly Detection CH Lai, D Zou, G Lerman Online proceedings of the Eighth International Conference on Learning …, 2020 | 67 | 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 | 66 | 2019 |
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 |
Robust stochastic principal component analysis J Goes, T Zhang, R Arora, G Lerman Proceedings of the 17th International Conference on Artificial Intelligence …, 2014 | 57 | 2014 |
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 | 49 | 2010 |
High-dimensional Menger-type curvatures. Part I: Geometric multipoles and multiscale inequalities G Lerman, JT Whitehouse Revista Matemática Iberoamericana 27 (2), 493-555, 2011 | 47 | 2011 |