  We introduce a new unsupervised anomaly detection ensemble called SPI which
can harness privileged information - data available only for training examples
but not for (future) test examples. Our ideas build on the Learning Using
Privileged Information (LUPI) paradigm pioneered by Vapnik et al. [19,17],
which we extend to unsupervised learning and in particular to anomaly
detection. SPI (for Spotting anomalies with Privileged Information) constructs
a number of frames/fragments of knowledge (i.e., density estimates) in the
privileged space and transfers them to the anomaly scoring space through
"imitation" functions that use only the partial information available for test
examples. Our generalization of the LUPI paradigm to unsupervised anomaly
detection shepherds the field in several key directions, including (i) domain
knowledge-augmented detection using expert annotations as PI, (ii) fast
detection using computationally-demanding data as PI, and (iii) early detection
using "historical future" data as PI. Through extensive experiments on
simulated and real datasets, we show that augmenting privileged information to
anomaly detection significantly improves detection performance. We also
demonstrate the promise of SPI under all three settings (i-iii); with PI
capturing expert knowledge, computationally expensive features, and future data
on three real world detection tasks.
