Detecting Anomaly in Chemical Sensors via L1-Kernel-Based Principal Component Analysis

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 letter, we introduce a new multiplication-free kernel, which is related to the $\ell {1}$-norm for the anomaly detection task. The $\ell {1}$-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 score (0.7483) than the baseline regular PCA method (0.7366).