Machine Learning, June 2018, Special Issue of the ECML PKDD 2018 Journal Track, ISSN: 0885-6125
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. The learning
of the proposed model is made tractable and scalable through the use of random
feature approximations and stochastic variational inference. The result is a
exible 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.
Type:
Journal
Date:
2018-06-14
Department:
Data Science
Eurecom Ref:
5589
Copyright:
© Springer. Personal use of this material is permitted. The definitive version of this paper was published in Machine Learning, June 2018, Special Issue of the ECML PKDD 2018 Journal Track, ISSN: 0885-6125 and is available at : http://doi.org/10.1007/s10994-018-5723-3
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