论文标题
有效的非线性RX异常检测器
Efficient Nonlinear RX Anomaly Detectors
论文作者
论文摘要
当前的异常检测算法通常受到准确性或效率的挑战。更准确的非线性探测器通常很慢，不可扩展。在这封信中，我们提出了两个技术系列，以提高标准内核Reed-Xiaoli（RX）方法的效率，以通过{\ em独立}随机傅立叶特征或{\ em em data Ildipentent}基于NyStröm的方法近似核函数来提高异常检测的效率。我们比较了真实多光谱图像的所有方法。我们表明，所提出的有效方法的计算成本较低，并且由于其隐式正则化效果，它们的标准内核RX算法的执行（或跑赢大度）相似（或跑赢大）。最后但并非最不重要的一点是，Nyström方法具有提高的检测能力。
Current anomaly detection algorithms are typically challenged by either accuracy or efficiency. More accurate nonlinear detectors are typically slow and not scalable. In this letter, we propose two families of techniques to improve the efficiency of the standard kernel Reed-Xiaoli (RX) method for anomaly detection by approximating the kernel function with either {\em data-independent} random Fourier features or {\em data-dependent} basis with the Nyström approach. We compare all methods for both real multi- and hyperspectral images. We show that the proposed efficient methods have a lower computational cost and they perform similar (or outperform) the standard kernel RX algorithm thanks to their implicit regularization effect. Last but not least, the Nyström approach has an improved power of detection.
