  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).
