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.

中文翻译：

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FROCC：基于快速随机投影的一类分类

我们提出基于快速随机投影的一类分类（FROCC），这是一种非常有效的一类分类方法。我们的方法基于一个简单的思想，即通过将训练数据投影到一组随机的单位向量上来转换训练数据，这些向量是独立于单位球面且独立地选择的，并基于数据的分离来界定区域。FROCC可以自然地用内核扩展。从理论上讲，我们证明FROCC具有稳定且偏差小的含义，因此泛化效果很好。FROCC的ROC最高可提高3.1个百分点，在一系列最先进的基准测试（包括SVM和基于OCC任务的基于深度学习的模型）的培训和测试时间上，可提高1.2--67.8倍的速度。