Incremental KPCA Using Mahalanobis Kernel for Outlier Detection in Wireless Sensor Networks

Abstract

Nowadays, Kernel PCA (KPCA) is considered as a natural nonlinear generalization of PCA, which extracts nonlinear structure from the data. In Wireless Sensor Networks (WSNs), the accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. For this case, the task amounts to create a useful model based on KPCA to recognize data as normal or outliers. Over the last years, Principal component analysis (PCA) has shown to be a good unsupervised feature extraction. But, this method only focuses on second orders statistics. Recently, Kernel Principal component analysis (KPCA) has used for nonlinear case which can extract higher order statistics. Kernel PCA (KPCA) maps the data onto another feature space and using nonlinear function. On account of the attractive capability, KPCA-based methods have been extensively investigated, and have showed excellent performance. So, we use an incremental KPCA based on Mahalanobis kernel as a pre-processing step to extract relevant feature for classification and to prevent from the abnormal events. All computation are done in the original space, thus saving computing time using Mahalanobis Kernel (MKPCA). Then the classification was done on real hyper spectral Intel Berkeley data from urban area. Results were positively compared to a version of a standard KPCA specially designed to be use with wireless sensor networks (WSNs).