Feature extraction method Based PCA and KICA

Kernel Independent Component Analysis (KICA) is a non-linear method for blind source separation (BSS) advanced recently. KICA can´t remove the disturbing noise in observed sample signal yet, it has a badness result of feature extraction. For these reason, paper gave a new method of feature extraction: PCA_KICA method, recurring to the characteristic of dimensional reduction and noise-removing of PCA. Simulation 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 PCA and PCA_ICA) by Amari error.