  A common machine learning task is to discriminate between normal and
anomalous data points. In practice, it is not always sufficient to reach high
accuracy at this task, one also would like to understand why a given data point
has been predicted in a certain way. We present a new principled approach for
one-class SVMs that decomposes outlier predictions in terms of input variables.
The method first recomposes the one-class model as a neural network with
distance functions and min-pooling, and then performs a deep Taylor
decomposition (DTD) of the model output. The proposed One-Class DTD is
applicable to a number of common distance-based SVM kernels and is able to
reliably explain a wide set of data anomalies. Furthermore, it outperforms
baselines such as sensitivity analysis, nearest neighbor, or simple edge
detection.
