  Outlier detection is a key field of machine learning for identifying abnormal
data objects. Due to the high expense of acquiring ground truth, unsupervised
models are often chosen in practice. To compensate for the unstable nature of
unsupervised algorithms, practitioners from high-stakes fields like finance,
health, and security, prefer to build a large number of models for further
combination and analysis. However, this poses scalability challenges in
high-dimensional large datasets. In this study, we propose a three-module
acceleration framework called SUOD to expedite the training and prediction with
a large number of unsupervised detection models. SUOD's Random Projection
module can generate lower subspaces for high-dimensional datasets while
reserving their distance relationship. Balanced Parallel Scheduling module can
forecast the training and prediction cost of models with high confidence---so
the task scheduler could assign nearly equal amount of taskload among workers
for efficient parallelization. SUOD also comes with a Pseudo-supervised
Approximation module, which can approximate fitted unsupervised models by lower
time complexity supervised regressors for fast prediction on unseen data. It
may be considered as an unsupervised model knowledge distillation process.
Notably, all three modules are independent with great flexibility to "mix and
match"; a combination of modules can be chosen based on use cases. Extensive
experiments on more than 30 benchmark datasets have shown the efficacy of SUOD,
and a comprehensive future development plan is also presented.
