论文标题
通过基于L1内核的主成分分析检测化学传感器中的异常
Detecting Anomaly in Chemical Sensors via L1-Kernels based Principal Component Analysis
论文作者
论文摘要
我们提出了一种基于内核PCA的方法来检测化学传感器中的异常。我们使用化学传感器产生的时间信号来形成向量以执行主成分分析（PCA）。我们估计传感器数据的内核互合矩阵，并计算与协方差矩阵最大的特征值相对应的特征向量。可以通过比较实际传感器数据与来自主要特征向量的重建数据之间的差异来检测异常。在本文中，我们引入了一个新的无乘法核，该核与与L1-norm有关的异常检测任务相关。 L1-kernel PCA不仅在计算上是有效的，而且是节能的，因为它在内核协方差矩阵计算过程中不需要任何实际的乘法。我们的实验结果表明，我们的内核PCA方法比基线常规PCA方法（0.7366）达到曲率（AUC）评分（0.7483）的面积更高。
We propose a kernel-PCA based method to detect anomaly in chemical sensors. We use temporal signals produced by chemical sensors to form vectors to perform the Principal Component Analysis (PCA). We estimate the kernel-covariance matrix of the sensor data and compute the eigenvector corresponding to the largest eigenvalue of the covariance matrix. The anomaly can be detected by comparing the difference between the actual sensor data and the reconstructed data from the dominant eigenvector. In this paper, we introduce a new multiplication-free kernel, which is related to the l1-norm for the anomaly detection task. The l1-kernel PCA is not only computationally efficient but also energy-efficient because it does not require any actual multiplications during the kernel covariance matrix computation. Our experimental results show that our kernel-PCA method achieves a higher area under curvature (AUC) score (0.7483) than the baseline regular PCA method (0.7366).
