Stream-Based Active Learning with Multiple Kernels

Jeongmin Chae, Songnam Hong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Online multiple kernel learning (OMKL) has provided an attractive performance in nonlinear function learning tasks. Leveraging a random feature (RF) approximation, the major drawback of OMKL, known as the curse of dimensionality, has been recently alleviated. These advantages enable RF-based OMKL to be considered in practice. In this paper we introduce a new research problem, named stream-based active multiple kernel learning (AMKL), where a learner is allowed to label some selected data from an oracle according to a selection criterion. This is necessary in many real-world applications since acquiring a true label is costly or time-consuming. We theoretically prove that the proposed AMKL achieves an optimal sublinear regret \mathcal{O}(\sqrt{T}) as in OMKL with little labeled data, implying that the proposed selection criterion indeed avoids unnecessary label-requests.

Original languageEnglish
Title of host publication35th International Conference on Information Networking, ICOIN 2021
PublisherIEEE Computer Society
Pages718-722
Number of pages5
ISBN (Electronic)9781728191003
DOIs
StatePublished - 2021 Jan 13
Event35th International Conference on Information Networking, ICOIN 2021 - Jeju Island, Korea, Republic of
Duration: 2021 Jan 132021 Jan 16

Publication series

NameInternational Conference on Information Networking
Volume2021-January
ISSN (Print)1976-7684

Conference

Conference35th International Conference on Information Networking, ICOIN 2021
Country/TerritoryKorea, Republic of
CityJeju Island
Period21/01/1321/01/16

Keywords

  • Active learning
  • multiple kernel learning
  • online learning
  • reproducing kernel Hilbert space

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