Please use this identifier to cite or link to this item:
|Title:||Fast transfer Gaussian process regression with large-scale sources||Authors:||Da, Bingshui
|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|
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.