FROCC: Fast Random projection-based One-Class Classification

TL;DR

FROCC is an efficient one-class classification method that uses random projections and can be extended with kernels, offering improved ROC performance and significant speedups over existing models.

Contribution

Introducing FROCC, a novel fast one-class classifier based on random projections with theoretical stability guarantees and kernel extension capabilities.

Findings

Achieves up to 3.1% better ROC performance.

Provides 1.2 to 67.8 times faster training and testing.

Outperforms state-of-the-art SVM and deep learning models.

Abstract

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.