Deep Gaussian Process autoencoders for novelty detection View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-06-14

AUTHORS

Rémi Domingues, Pietro Michiardi, Jihane Zouaoui, Maurizio Filippone

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. More... »

PAGES

1363-1383

References to SciGraph publications

  • 2004-10. A Survey of Outlier Detection Methodologies in ARTIFICIAL INTELLIGENCE REVIEW
  • 1996. Bayesian Learning for Neural Networks in NONE
  • 2015-05-27. Deep learning in NATURE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10994-018-5723-3

    DOI

    http://dx.doi.org/10.1007/s10994-018-5723-3

    DIMENSIONS

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