Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142637
Title: Fast transfer Gaussian process regression with large-scale sources
Authors: Da, Bingshui
Ong, Yew-Soon
Gupta, Abhishek
Feng, Liang
Liu, Haitao
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Da, B., Ong, Y.-S., Gupta, A., Feng, L., & Liu, H. (2019). Fast transfer Gaussian process regression with large-scale sources. Knowledge-Based Systems, 165, 208-218. doi:10.1016/j.knosys.2018.11.029
Journal: Knowledge-Based Systems
Abstract: In transfer learning, we aim to improve the predictive modeling of a target output by using the knowledge from some related source outputs. In real-world applications, the data from the target domain is often precious and hard to obtain, while the data from source domains is plentiful. Thus, since the complexity of Gaussian process based multi-task/transfer learning approaches grows cubically with the total number of source+ target observations, the method becomes increasingly impractical for large () source data inputs even with a small amount of target data. In order to scale known transfer Gaussian processes to large-scale source datasets, we propose an efficient aggregation model in this paper, which combines the predictions from distributed (small-scale) local experts in a principled manner. The proposed model inherits the advantages of single-task aggregation schemes, including efficient computation, analytically tractable inference, and straightforward parallelization during training and prediction. Further, a salient feature of the proposed method is the enhanced expressiveness in transfer learning — as a byproduct of flexible inter-task relationship modelings across different experts. When deploying such models in real-world applications, each local expert corresponds to a lightweight predictor that can be embedded in edge devices, thus catering to cases of online on-mote processing in fog computing settings.
URI: https://hdl.handle.net/10356/142637
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2018.11.029
Rights: © 2018 Elsevier B.V. All rights reserved. This paper was published in Knowledge-Based Systems and is made available with permission of Elsevier B.V.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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