FROCC: Fast Random projection-based One-Class Classification
This method offers a significantly faster and more accurate one-class classification solution for practitioners and researchers working with anomaly detection.
This paper introduces FROCC, a one-class classification method that uses random projections to transform training data and bound regions. FROCC achieves up to 3.1 percentage points better ROC and 1.2-67.8x speedup in training and test times compared to state-of-the-art benchmarks.
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.