| Multiscale geometric methods for data sets I: Multiscale SVD, noise and curvature AV Little, M Maggioni, L Rosasco Applied and Computational Harmonic Analysis 43 (3), 504-567, 2017 | 105 | 2017 |
| Estimation of intrinsic dimensionality of samples from noisy low-dimensional manifolds in high dimensions with multiscale SVD AV Little, J Lee, YM Jung, M Maggioni 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, 85-88, 2009 | 76 | 2009 |
| Multiscale Estimation of Intrinsic Dimensionality of Data Sets. AV Little, YM Jung, M Maggioni AAAI fall symposium: manifold learning and its applications 9, 04, 2009 | 73 | 2009 |
| Path-based spectral clustering: Guarantees, robustness to outliers, and fast algorithms A Little, M Maggioni, JM Murphy Journal of machine learning research 21 (6), 1-66, 2020 | 51 | 2020 |
| Some recent advances in multiscale geometric analysis of point clouds G Chen, AV Little, M Maggioni, L Rosasco Wavelets and Multiscale Analysis: Theory and Applications, 199-225, 2011 | 24 | 2011 |
| Multi-resolution geometric analysis for data in high dimensions G Chen, AV Little, M Maggioni Excursions in Harmonic Analysis, Volume 1: The February Fourier Talks at the …, 2013 | 22 | 2013 |
| Multiscale geometric methods for estimating intrinsic dimension AV Little, M Maggioni, L Rosasco Proc. SampTA 4 (2), 2011 | 21 | 2011 |
| An analysis of classical multidimensional scaling with applications to clustering A Little, Y Xie, Q Sun Information and Inference: A Journal of the IMA 12 (1), 72-112, 2023 | 18 | 2023 |
| Path-based spectral clustering: Guarantees, robustness to outliers, and fast algorithms A Little, M Maggioni, JM Murphy arXiv preprint arXiv:1712.06206, 2017 | 18 | 2017 |
| Taxonomy of benchmarks in graph representation learning R Liu, S Cantürk, F Wenkel, S McGuire, X Wang, A Little, L O’Bray, ... Learning on Graphs Conference, 6: 1-6: 25, 2022 | 17 | 2022 |
| Balancing geometry and density: Path distances on high-dimensional data A Little, D McKenzie, JM Murphy SIAM Journal on Mathematics of Data Science 4 (1), 72-99, 2022 | 17 | 2022 |
| A multiscale spectral method for learning number of clusters A Little, A Byrd 2015 IEEE 14th International Conference on Machine Learning and Applications …, 2015 | 14 | 2015 |
| Spectral clustering technique for classifying network attacks A Little, X Mountrouidou, D Moseley 2016 IEEE 2nd International Conference on Big Data Security on Cloud …, 2016 | 12 | 2016 |
| Estimating the intrinsic dimension of high-dimensional data sets: a multiscale, geometric approach AV Little Duke University, 2011 | 11 | 2011 |
| An analysis of classical multidimensional scaling A Little, Y Xie, Q Sun arXiv preprint arXiv:1812.11954, 2018 | 10 | 2018 |
| Multiscale geometric methods for data sets I: Estimation of intrinsic dimension A Little, M Maggioni, L Rosasco preparation, 2010 | 10 | 2010 |
| Wavelet invariants for statistically robust multi-reference alignment M Hirn, A Little Information and Inference: A Journal of the IMA 10 (4), 1287-1351, 2021 | 8 | 2021 |
| Fermat distances: Metric approximation, spectral convergence, and clustering algorithms NG Trillos, A Little, D McKenzie, JM Murphy arXiv preprint arXiv:2307.05750, 2023 | 5 | 2023 |
| Balancing geometry and density: Path distances on high-dimensional data A Little, D McKenzie, J Murphy arXiv preprint arXiv:2012.09385, 2020 | 3 | 2020 |
| Positive Solutions to a Diffusive Logistic Equation with Constant Yield Harvesting T Ladner, A Little, K Marks, A Russell Rose-Hulman Undergraduate Mathematics Journal 6 (1), 3, 2005 | 3 | 2005 |
Mauro MaggioniBloomberg Distinguished Professor of Mathematics, and Applied Mathematics and StatisticsEmail được xác minh tại jhu.edu