Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139612
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dc.contributor.authorLiu, Haitaoen_US
dc.contributor.authorCai, Jianfeien_US
dc.contributor.authorOng, Yew-Soonen_US
dc.date.accessioned2020-05-20T08:15:25Z-
dc.date.available2020-05-20T08:15:25Z-
dc.date.issued2018-
dc.identifier.citationLiu, H., Cai, J., & Ong, Y.-S. (2018). Remarks on multi-output Gaussian process regression. Knowledge-Based Systems, 144, 102-121. doi:10.1016/j.knosys.2017.12.034en_US
dc.identifier.issn0950-7051en_US
dc.identifier.urihttps://hdl.handle.net/10356/139612-
dc.description.abstractMulti-output regression problems have extensively arisen in modern engineering community. This article investigates the state-of-the-art multi-output Gaussian processes (MOGPs) that can transfer the knowledge across related outputs in order to improve prediction quality. We classify existing MOGPs into two main categories as (1) symmetric MOGPs that improve the predictions for all the outputs, and (2) asymmetric MOGPs, particularly the multi-fidelity MOGPs, that focus on the improvement of high fidelity output via the useful information transferred from related low fidelity outputs. We review existing symmetric/asymmetric MOGPs and analyze their characteristics, e.g., the covariance functions (separable or non-separable), the modeling process (integrated or decomposed), the information transfer (bidirectional or unidirectional), and the hyperparameter inference (joint or separate). Besides, we assess the performance of ten representative MOGPs thoroughly on eight examples in symmetric/asymmetric scenarios by considering, e.g., different training data (heterotopic or isotopic), different training sizes (small, moderate and large), different output correlations (low or high), and different output sizes (up to four outputs). Based on the qualitative and quantitative analysis, we give some recommendations regarding the usage of MOGPs and highlight potential research directions.en_US
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en_US
dc.language.isoenen_US
dc.relation.ispartofKnowledge-Based Systemsen_US
dc.rights© 2018 Elsevier B.V. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleRemarks on multi-output Gaussian process regressionen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.organizationRolls-Royce@NTU Corporate Laben_US
dc.contributor.organizationData Science and Artificial Intelligence Research Centeren_US
dc.identifier.doi10.1016/j.knosys.2017.12.034-
dc.identifier.scopus2-s2.0-85040123192-
dc.identifier.volume144en_US
dc.identifier.spage102en_US
dc.identifier.epage121en_US
dc.subject.keywordsMulti-output Gaussian Processen_US
dc.subject.keywordsSymmetric/asymmetric MOGPen_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
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