Dynamic Barycenter Averaging Kernel in RBF Networks for Time Series Classification

Radial basis function (RBF) network has been utilized in many applications due to its simple topological structure and strong capacity on function approximation. The core of RBF network is its static kernel function, which is based on the Euclidean distance and cannot obtain good performance for time series classification (TSC) due to the time-shift invariance, complex dynamics, and different length of temporal data.This paper proposed a new temporal kernel, namely, the dynamic barycenter averaging kernel (DBAK) and introduced it into the RBF network. First, we combine k-means clustering with a dynamic time warping (DTW)-based averaging algorithm called DTW barycenter averaging (DBA) to determine the center of DBAK. Then, in order to facilitate the stable gradient-training process in the whole network, a normalization term is added into the kernel formulation. By integrating the information of the whole time warping path, our DBAK-based RBF network performs efficiently for TSC tasks.