Name
Abstract | ||
|---|---|---|
Gaussian process (GP) for machine learning has been well studied over the past two decades and is now widely used in many sectors. However, the design of low-complexity GP models still remains a challenging research problem. In this paper, we propose a novel scalable GP regression model for processing big datasets, using a large number of parallel computation units. In contrast to the existing methods, we solve the classic maximum likelihood based hyper-parameter optimization problem by a carefully designed distributed alternating direction method of multipliers (ADMM). The proposed method is parallelizable over a large number of computation units. Simulation results confirm the benefits of the proposed scalable GP model over the state-of-the-art distributed methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8682350 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
ADMM, big data, Gaussian process, hyper-parameter optimization, scalable model | Kernel (linear algebra),Parallelizable manifold,Mathematical optimization,Computer science,Regression analysis,Parallel computing,Gaussian process,Big data,Optimization problem,Scalability,Computation | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Xu | 1 | 199 | 23.99 |
| Feng Yin | 2 | 2 | 1.38 |
| Jiawei Zhang | 3 | 806 | 72.17 |
| Wenjun Xu | 4 | 45 | 11.81 |
| Shuguang Cui | 5 | 5382 | 368.45 |
| Zhi-Quan Luo | 6 | 7506 | 598.19 |