Online learning of sparse pseudo-input Gaussian process

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

Abstract

In this paper, we propose a novel method of online learning of sparse pseudo-data, representative of the whole training data, for Gaussian Process (GP) regressions. We call the proposed method Incremental Sparse Pseudo-input Gaussian Process (ISPGP) regression. The proposed ISPGP algorithm allows for training from either a huge amount of training data by scanning through it only once or an online incremental training dataset. Thanks to the nature of the incremental learning algorithm, the proposed ISPGP algorithm can theoretically work with infinite data to which the conventional GP or SPGP algorithm is not applicable. From our experimental results on the KIN40K dataset, we can see that the proposed ISPGP algorithm is comparable to the conventional GP algorithm using the same number of training data. Although the proposed ISPGP algorithm performs slightly worse than Snelson and Ghahramani's SPGP algorithm, the level of performance degradation is acceptable.

Original languageEnglish
Title of host publicationProceedings 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
Pages1357-1360
Number of pages4
DOIs
Publication statusPublished - 2012
Event2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 - Seoul, Korea, Republic of
Duration: 2012 Oct 142012 Oct 17

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Other

Other2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
CountryKorea, Republic of
CitySeoul
Period12/10/1412/10/17

Keywords

  • Gaussian process regression
  • incremental learning
  • pseudo-input

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

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