Comparison of SFA and ICA

Abstract

Recently, a new method that slow feature analysis (SFA), which can extract slowly varying feature of temporally varying signals, has been explored. SFA method is an extension of independent component analysis (ICA), which has been used to separate blind source signals. In this article, we present a simple and efficient SFA based method to separate blind signals according to their different smooth degree. The performance of the proposed mathod is higher than that of the conventional method ICA. Simulation illustrates the good performance of the proposed method.