| Graph learning from data under laplacian and structural constraints HE Egilmez, E Pavez, A Ortega IEEE Journal of Selected Topics in Signal Processing, 2017 | 368 | 2017 |
| Generalized Laplacian precision matrix estimation for graph signal processing E Pavez, A Ortega IEEE International Conference on Acoustics, Speech and Signal Processing …, 2016 | 122 | 2016 |
| Graph learning from filtered signals: Graph system and diffusion kernel identification HE Egilmez, E Pavez, A Ortega IEEE Transactions on Signal and Information Processing over Networks 5 (2 …, 2018 | 65 | 2018 |
| Learning graphs with monotone topology properties and multiple connected components E Pavez, HE Egilmez, A Ortega IEEE Transactions on Signal Processing 66 (9), 2399-2413, 2018 | 59 | 2018 |
| Dynamic polygon clouds: representation and compression for VR/AR E Pavez, PA Chou, RL De Queiroz, A Ortega APSIPA Transactions on Signal and Information Processing 7, 2018 | 49* | 2018 |
| Analysis and design of wavelet-packet cepstral coefficients for automatic speech recognition E Pavez, JF Silva Speech Communication 54 (6), 814-835, 2012 | 47 | 2012 |
| GTT: Graph Template Transforms with Applications to Image Coding E Pavez, HE Egilmez, Y Wang, A Ortega Picture Coding Symposium (PCS), 2015, 199-203, 2015 | 42 | 2015 |
| System and method for inter-frame predictive compression for point clouds D Tian, E Pavez, R Cohen, A Vetro US Patent 10,499,054, 2019 | 34 | 2019 |
| Dynamic polygon cloud compression E Pavez, PA Chou Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International …, 2017 | 30 | 2017 |
| Region adaptive graph Fourier transform for 3D point clouds E Pavez, B Girault, A Ortega, PA Chou 2020 IEEE International Conference on Image Processing (ICIP), 2726-2730, 2020 | 25 | 2020 |
| Covariance matrix estimation with non uniform and data dependent missing observations E Pavez, A Ortega IEEE Transactions on Information Theory 67 (2), 1201-1215, 2020 | 16 | 2020 |
| Graph learning with Laplacian constraints: Modeling attractive Gaussian Markov random fields HE Egilmez, E Pavez, A Ortega 2016 50th Asilomar Conference on Signals, Systems and Computers, 1470-1474, 2016 | 10 | 2016 |
| GLL: Graph Laplacian learning package, version 1.0 HE Egilmez, E Pavez, A Ortega Graph_Learning, 2017 | 9 | 2017 |
| Two channel filter banks on arbitrary graphs with positive semi definite variation operators E Pavez, B Girault, A Ortega, PA Chou IEEE Transactions on Signal Processing 71, 917-932, 2023 | 8 | 2023 |
| A graph learning algorithm based on Gaussian Markov random fields and minimax concave penalty T Koyakumaru, M Yukawa, E Pavez, A Ortega ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 8 | 2021 |
| Learning separable transforms by inverse covariance estimation E Pavez, A Ortega, D Mukherjee International Conference on Image Processing (ICIP) 2017, 2017 | 8 | 2017 |
| Multi-resolution intra-predictive coding of 3d point cloud attributes E Pavez, AL Souto, RL De Queiroz, A Ortega 2021 IEEE International Conference on Image Processing (ICIP), 3393-3397, 2021 | 7 | 2021 |
| Spectral folding and two-channel filter-banks on arbitrary graphs E Pavez, B Girault, A Ortega, PA Chou ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 7 | 2021 |
| On learning laplacians of tree structured graphs KS Lu, E Pavez, A Ortega 2018 IEEE Data Science Workshop (DSW), 205-209, 2018 | 7 | 2018 |
| Learning sparse graph with minimax concave penalty under Gaussian Markov random fields T Koyakumaru, M Yukawa, E Pavez, A Ortega IEICE Transactions on Fundamentals of Electronics, Communications and …, 2023 | 5 | 2023 |
Antonio OrtegaDean's Professor of Electrical and Computer Engineering, University of Southern CaliforniaVerified email at usc.edu
