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.
Deep Gaussian process autoencoders for novelty detection
ECML-PKDD 2018, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Journal Track, 10-14 September 2018, Dublin, Ireland
© Springer. Personal use of this material is permitted. The definitive version of this paper was published in ECML-PKDD 2018, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Journal Track, 10-14 September 2018, Dublin, Ireland and is available at : http://doi.org/10.1007/s10994-018-5723-3
PERMALINK : https://www.eurecom.fr/publication/5695