| A nonlinear dimensionality reduction framework using smooth geodesics K Gajamannage, R Paffenroth, EM Bollt Pattern Recognition 87, 226-236, 2019 | 26 | 2019 |
| Real-time forecasting of time series in financial markets using sequentially trained dual-LSTMs K Gajamannage, Y Park, DI Jayathilake Expert Systems with Applications 223, 119879, 2023 | 24 | 2023 |
| Dimensionality reduction of collective motion by principal manifolds K Gajamannage, S Butail, M Porfiri, EM Bollt Physica D: Nonlinear Phenomena 291, 62-73, 2015 | 19 | 2015 |
| Network topology mapping from partial virtual coordinates and graph geodesics AP Jayasumana, R Paffenroth, G Mahindre, S Ramasamy, ... IEEE/ACM Transactions on Networking 27 (6), 2405-2417, 2019 | 18 | 2019 |
| Identifying manifolds underlying group motion in Vicsek agents K Gajamannage, S Butail, M Porfiri, EM Bollt The European Physical Journal Special Topics 224, 3245-3256, 2015 | 14 | 2015 |
| On sampling and recovery of topology of directed social networks–a low-rank matrix completion based approach G Mahindre, AP Jayasumana, K Gajamannage, R Paffenroth 2019 IEEE 44th Conference on Local Computer Networks (LCN), 324-331, 2019 | 11 | 2019 |
| Dimenslon estlmatlon of equlty markets N Bahadur, R Paffenroth, K Gajamannage 2019 IEEE International Conference on Big Data (Big Data), 5491-5498, 2019 | 10 | 2019 |
| Detecting phase transitions in collective behavior using manifold's curvature K Gajamannage, EM Bollt Mathematical Biosciences and Engineering 14 (2), 437-453, 2017 | 10 | 2017 |
| Recurrent neural networks for dynamical systems: Applications to ordinary differential equations, collective motion, and hydrological modeling K Gajamannage, DI Jayathilake, Y Park, EM Bollt Chaos: An Interdisciplinary Journal of Nonlinear Science 33 (1), 2023 | 9 | 2023 |
| Recurrent neural networks for dynamical systems: Applications to ordinary differential equations, collective motion, and hydrological modeling Y Park, K Gajamannage, DI Jayathilake, EM Bollt arXiv preprint arXiv:2202.07022, 2022 | 9 | 2022 |
| Bounded manifold completion K Gajamannage, R Paffenroth Pattern Recognition 111, 107661, 2021 | 9 | 2021 |
| Fraud detection using optimized machine learning tools under imbalance classes M Isangediok, K Gajamannage 2022 IEEE International Conference on Big Data (Big Data), 4275-4284, 2022 | 8 | 2022 |
| Reconstruction of fragmented trajectories of collective motion using Hadamard deep autoencoders K Gajamannage, Y Park, R Paffenroth, AP Jayasumana Pattern Recognition 131, 108891, 2022 | 6 | 2022 |
| A Patch-based Image Denoising Method Using Eigenvectors of the Geodesics' Gramian Matrix K Gajamannage, R Paffenroth, AP Jayasumana arXiv preprint arXiv:2010.07769, 2020 | 5 | 2020 |
| Modeling the lowest-cost splitting of a herd of cows by optimizing a cost function K Gajamannage, EM Bollt, MA Porter, MS Dawkins Chaos: An Interdisciplinary Journal of Nonlinear Science 27 (6), 2017 | 5 | 2017 |
| Reconstruction of agents’ corrupted trajectories of collective motion using low-rank matrix completion K Gajamannage, R Paffenroth 2019 IEEE International Conference on Big Data (Big Data), 2826-2834, 2019 | 3 | 2019 |
| Geodesic Gramian denoising applied to the images contaminated with noise sampled from diverse probability distributions K Gajamannage, Y Park, A Sadovski IET Image Processing 17 (1), 144-156, 2023 | 1 | 2023 |
| Efficient noise filtration of images by low-rank singular vector approximations of Geodesics' Gramian Matrix K Gajamannage, Y Park, M Muddamallappa, S Mathur arXiv preprint arXiv:2209.13094, 2022 | | 2022 |
| Geodesic Gramian Denoising Applied to the Images Contaminated With Noise Sampled From Diverse Probability Distributions Y Park, K Gajamannage, A Sadovski arXiv preprint arXiv:2203.02600, 2022 | | 2022 |
| Manifold Learning and Dimensionality Reduction in Collective Motion KD Gajamannage Clarkson University, 2016 | | 2016 |
