Erratum: Gaussian Process Online Learning with a Sparse Data Stream (IEEE Robot. Autom. Lett. (2020) 5: 4 (5977-5984) DOI: 10.1109/LRA.2020.3010752)

Jaehyun Lim; Jehyun Park; Jongeun Choi

doi:10.1109/LRA.2020.3043964

Erratum: Gaussian Process Online Learning with a Sparse Data Stream (IEEE Robot. Autom. Lett. (2020) 5: 4 (5977-5984) DOI: 10.1109/LRA.2020.3010752)

Jaehyun Lim, Jehyun Park, Jongeun Choi

Department of Mechanical Engineering

Research output: Contribution to journal › Comment/debate › peer-review

Abstract

A letter entitled 'Gaussian Process Online Learning With a Sparse Data Stream' suggests an approach for extending the infinite-horizon Gaussian processes (IHGPs, [1]) 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.

Original language	English
Article number	9310363
Pages (from-to)	429-430
Number of pages	2
Journal	IEEE Robotics and Automation Letters
Volume	6
Issue number	2
DOIs	https://doi.org/10.1109/LRA.2020.3043964
Publication status	Published - 2021 Apr

Bibliographical note

Funding 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: jaehyunlim@yonsei.ac.kr; jhyunpark@yonsei.ac.kr; jongeunchoi@ yonsei.ac.kr). Digital Object Identifier 10.1109/LRA.2020.3043964

Publisher Copyright:
© 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

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abstract = "A letter entitled 'Gaussian Process Online Learning With a Sparse Data Stream' suggests an approach for extending the infinite-horizon Gaussian processes (IHGPs, [1]) 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.",

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note = "Funding 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: jaehyunlim@yonsei.ac.kr; jhyunpark@yonsei.ac.kr; jongeunchoi@ yonsei.ac.kr). Digital Object Identifier 10.1109/LRA.2020.3043964 Publisher Copyright: {\textcopyright} 2016 IEEE.",

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Erratum: Gaussian Process Online Learning with a Sparse Data Stream (IEEE Robot. Autom. Lett. (2020) 5: 4 (5977-5984) DOI: 10.1109/LRA.2020.3010752)

Abstract

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