KICA-based feature extraction for mechanical noise data

Kernel Independent Component Analysis (KICA) which is advanced recently is a non-linear method for blind source separation (BSS). KICA can't reduce the dimension of multidimensional data when extract its feature, that is to say, KICA can't remove the disturbing noise in observed sample signal. For these reason, paper improved its ability to process the multidimensional data, recurring to the characteristic of dimensional reduction and noise-removing of PCA. Then paper used this method to process the mechanical noise data. Results of example show that PCA_KICA method can be used to remove the disturbing noise availably, and also to separate the original signal accurately. It has a better result compared with other feature extraction methods (such as ICA) by Amari error.