  We present Fast Random projection-based One-Class Classification (FROCC), an
extremely efficient method for one-class classification. Our method is based on
a simple idea of transforming the training data by projecting it onto a set of
random unit vectors that are chosen uniformly and independently from the unit
sphere, and bounding the regions based on separation of the data. FROCC can be
naturally extended with kernels. We theoretically prove that FROCC generalizes
well in the sense that it is stable and has low bias. FROCC achieves up to 3.1
percent points better ROC, with 1.2--67.8x speedup in training and test times
over a range of state-of-the-art benchmarks including the SVM and the deep
learning based models for the OCC task.
