A Robust Kernel Based on Robust ρ-Function

Chia-Te Liao, Shang-Hong Lai

Abstract: Noise-resistance capability is a very important issue to signal processing systems as well as machine learning applications. In this work, we present a new kernel that is highly robust against outliers and random noises. By incorporating a robust ρ-function into the distance metric, the derived robust kernel was shown to be very insensitive to the influence of outlier elements. In the experiments, we show that the proposed kernel brought significant improvement to the support vector machines (SVM) classifier in face recognition accuracy and outperformed several traditional kernels for corrupted data. We also applied our kernel to the kernel principal component analysis (PCA) and evaluate the efficiency in recovering contaminated face images. Experiments show our robust kernel also brings benefits in noise-reduction applications.