Abstract
This paper presents a novel online sparse Gaussian process (GP) approximation method [3] that is capable of achieving constant time and memory (i.e., independent of the size of the data) per time step. We theoretically guarantee its predictive performance to be equivalent to that of a sophisticated offline sparse GP approximation method. We empirically demonstrate the practical feasibility of using our online sparse GP approximation method through a real-world persistent mobile robot localization experiment.
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Xu, N., Low, K.H., Chen, J., Lim, K.K., Özgül, E.B.: GP-Localize: Persistent mobile robot localization using online sparse Gaussian process observation model. In: Proc. AAAI (2014)
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Low, K.H., Xu, N., Chen, J., Lim, K.K., Özgül, E.B. (2014). Generalized Online Sparse Gaussian Processes with Application to Persistent Mobile Robot Localization. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2014. Lecture Notes in Computer Science(), vol 8726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44845-8_44
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DOI: https://doi.org/10.1007/978-3-662-44845-8_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44844-1
Online ISBN: 978-3-662-44845-8
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