  Detecting and explaining anomalies is a challenging effort. This holds
especially true when data exhibits strong dependencies and single measurements
need to be assessed and analyzed in their respective context. In this work, we
consider scenarios where measurements are non-i.i.d, i.e. where samples are
dependent on corresponding discrete latent variables which are connected
through some given dependency structure, the contextual information. Our
contribution is twofold: (i) Building atop of support vector data description
(SVDD), we derive a method able to cope with latent-class dependency structure
that can still be optimized efficiently. We further show that our approach
neatly generalizes vanilla SVDD as well as k-means and conditional random
fields (CRF) and provide a corresponding probabilistic interpretation. (ii) In
unsupervised scenarios where it is not possible to quantify the accuracy of an
anomaly detector, having an human-interpretable solution is the key to success.
Based on deep Taylor decomposition and a reformulation of our trained anomaly
detector as a neural network, we are able to backpropagate predictions to
pixel-domain and thus identify features and regions of high relevance. We
demonstrate the usefulness of our novel approach on toy data with known
spatio-temporal structure and successfully validate on synthetic as well as
real world off-shore data from the oil industry.
