A letter entitled 'Gaussian Process Online Learning With a Sparse Data Stream' suggests an approach for extending the infinite-horizon Gaussian processes (IHGPs, ) to deal with a sparse data stream. We point out that there is an error in differentiating the discrete algebraic Riccati equation (DARE), which significantly changes the results of the benchmarking study in a sense that the proposed approach using the solution of the Lyapunov equation does not show outperformance against the original IHGP. In this letter, we provide a correction with details and its consequential implication.
Bibliographical noteFunding Information:
Manuscript received August 12, 2020; accepted August 12, 2020. Date of current version December 28, 2020. This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2018R1A4A1025986), and in part by the Mid-career Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, and ICT (NRF-2018R1A2B6008063). (Corresponding author: Jongeun Choi.) The authors are with the School of Mechanical Engineering, Yonsei University, 50 Yonsei Ro, Seodaemun Gu, Seoul 03722, South Korea (e-mail: firstname.lastname@example.org; email@example.com; jongeunchoi@ yonsei.ac.kr). Digital Object Identifier 10.1109/LRA.2020.3043964
© 2016 IEEE.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Biomedical Engineering
- Human-Computer Interaction
- Mechanical Engineering
- Computer Vision and Pattern Recognition
- Computer Science Applications
- Control and Optimization
- Artificial Intelligence