Enhancing the Adaptiveness of Gaussian Process Regression based on Power Spectral Density

Dinh Mao Bui, Nguyen Anh Tu, Kok Seng Wong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In many years, Gaussian process was popularly utilized in many research areas such as signal processing, data communications and image processing, etc. Unlike other techniques which try to determine all of the parameters of system model, Gaussian process adapts these parameters to reflect the actual underlying model. Because of that, this approach can be explicitly addressed as a non-parametric methodology. As a comparison to other well-known methods, Gaussian process regression (GPR) possesses much better performance in terms of precision and versatility. However, this technique does have some drawbacks. One of them is the adaptiveness to the complex data. In this research, we would like to introduce a novel solution based on power spectral density to adapt the model for better accuracy.

Original languageEnglish
Title of host publicationProceedings of the 2020 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020
EditorsSukhan Lee, Hyunseung Choo, Roslan Ismail
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728154534
DOIs
Publication statusPublished - Jan 2020
Event14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020 - Taichung, Taiwan
Duration: Jan 3 2020Jan 5 2020

Publication series

NameProceedings of the 2020 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020

Conference

Conference14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020
CountryTaiwan
CityTaichung
Period1/3/201/5/20

Keywords

  • Complex data
  • Domain transformation
  • Gaussian process regression
  • Non-parametric regression
  • Power spectral density

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Information Systems and Management
  • Communication

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