Deep Gaussian Process autoencoders for novelty detection

Rémi Domingues*, Pietro Michiardi, Jihane Zouaoui, Maurizio Filippone

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Novelty detection is one of the classic problems in machine learning that has applications across several domains. This paper proposes a novel autoencoder based on Deep Gaussian Processes for novelty detection tasks. Learning the proposed model is made tractable and scalable through the use of random feature approximations and stochastic variational inference. The result is a flexible model that is easy to implement and train, and can be applied to general novelty detection tasks, including large-scale problems and data with mixed-type features. The experiments indicate that the proposed model achieves competitive results with state-of-the-art novelty detection methods.

Original languageEnglish (US)
Pages (from-to)1363-1383
Number of pages21
JournalMachine Learning
Volume107
Issue number8-10
DOIs
StatePublished - Sep 1 2018

Keywords

  • Autoencoder
  • Deep Gaussian Processes
  • Novelty detection
  • Stochastic variational inference
  • Unsupervised learning

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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