Edge effects of BEMD improved by expansion of support-vector-regression extrapolation and mirror-image signals

In the operation of bidimensional empirical mode decomposition, expansion with mirror-image signals is an effective approach to weaken the edge effect. To meet the basic requirement that mirrors should be placed at the extrema, however, there is a problem to make full use of the information involved in the original signal. To address this problem, we propose an approach with the expansion of both support-vector-regression (SVR) extrapolation and mirror-image signals, in which the extrema are captured from the data of SVR extrapolation. The SVR model is constructed with the support vector method (SVM) based on the original signal data. Its extrapolation results in the estimation of the signal data beyond the edge for capturing the extrema so that the information of the original signal can be fully used in locating the mirror. Once all of these extrema points are determined, the traditional mirror expansion method is used and finally edge effects of the BEMD are eliminated. Results from numerical experiments show that the proposed approach has a good capability of improving edge effects of the BEMD operation process, and the reconstruction image from the decomposed components of the intrinsic mode function (IMF) confirms its high coherency with the original one.