Dynamic Barycenter Averaging Kernel in RBF Networks for Time Series Classification

IEEE Access

Lifeng Shen¹  Hongyang Qin¹  Sen Li¹  Qianli Ma¹  Kejian Shi¹  Chijun Sima¹ 

[1] School of Computer Science and Engineering, South China University of Technology, Guangzhou, China;

关键词: Radial basis function;    dynamic time warping;    kernel function;    time series classification;   

DOI  :  10.1109/ACCESS.2019.2910017

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown