  Novelty detection is the unsupervised problem of identifying anomalies in
test data which significantly differ from the training set. Novelty detection
is one of the classic challenges in Machine Learning and a core component of
several research areas such as fraud detection, intrusion detection, medical
diagnosis, data cleaning, and fault prevention. While numerous algorithms were
designed to address this problem, most methods are only suitable to model
continuous numerical data. Tackling datasets composed of mixed-type features,
such as numerical and categorical data, or temporal datasets describing
discrete event sequences is a challenging task. In addition to the supported
data types, the key criteria for efficient novelty detection methods are the
ability to accurately dissociate novelties from nominal samples, the
interpretability, the scalability and the robustness to anomalies located in
the training data.
  In this thesis, we investigate novel ways to tackle these issues. In
particular, we propose (i) an experimental comparison of novelty detection
methods for mixed-type data (ii) an experimental comparison of novelty
detection methods for sequence data, (iii) a probabilistic nonparametric
novelty detection method for mixed-type data based on Dirichlet process
mixtures and exponential-family distributions and (iv) an autoencoder-based
novelty detection model with encoder/decoder modelled as deep Gaussian
processes.
