Gaussian process regression is a powerful machine learning method to solve non-linear regression problems. However, because of the intensive computation, Gaussian process regression is not suitable for large-scale machine learning problems. Fortunately, researchers developed approximation methods to get a solution arbitrarily close as the original Gaussian process more rapidly and with better scaling. In this post, a Random Fourier Features accelerated Gaussian process regressor will be introduced.
The 2022 HPCC Systems Intern Program is well underway with 11 students working on projects this summer. Meet the students, learn more about their projects and find out more about the progress they are making as they work with their mentors and other members of the HPCC Systems Platform Team.