Classification of multiple annotator data using variational Gaussian process inference

Emre Besler, Pablo Ruiz, Rafael Molina, Aggelos K Katsaggelos

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

2 Scopus citations

Abstract

In this paper we address supervised learning problems where, instead of having a single annotator who provides the ground truth, multiple annotators, usually with varying degrees of expertise, provide conflicting labels for the same sample. Once Gaussian Process classification has been adapted to this problem we propose and describe how Variational Bayes inference can be used to, given the observed labels, approximate the posterior distribution of the latent classifier and also estimate each annotator's reliability. In the experimental section, we evaluate the proposed method on both generated synthetic and real data, and compare it with state of the art crowdsourcing methods.

Original languageEnglish (US)
Title of host publication2016 24th European Signal Processing Conference, EUSIPCO 2016
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages2025-2029
Number of pages5
Volume2016-November
ISBN (Electronic)9780992862657
DOIs
StatePublished - Nov 28 2016
Event24th European Signal Processing Conference, EUSIPCO 2016 - Budapest, Hungary
Duration: Aug 28 2016Sep 2 2016

Other

Other24th European Signal Processing Conference, EUSIPCO 2016
Country/TerritoryHungary
CityBudapest
Period8/28/169/2/16

Keywords

  • Bayesian modeling
  • Classification
  • Crowdsourcing
  • Gaussian process
  • Multiple labels
  • Variational inference

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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