论文标题
FROCC：基于快速随机投影的一级分类
FROCC: Fast Random projection-based One-Class Classification
论文作者
论文摘要
我们提出了基于快速的随机投影一流分类（FROCC），这是一种非常有效的一级分类方法。我们的方法是基于一个简单的想法，即通过将训练数据投影到一组随机单位向量上，这些向量是独立于单位球体统一和独立选择的，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，我们证明了Frocc在稳定且偏见低的意义上概括了。 FROCC在ROC中获得了高达3.1％的分数，在训练和测试时间的1.2--67.8倍加速度上，包括SVM和OCC任务的基于深度学习的模型，包括SVM和基于深度学习的模型。
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.
