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 cant reduce the dimension of multidimensional data when extract its feature, that is to say, KICA cant 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.
data mining feature extraction mechanical noise data PCA_KICA Amari error
Sheng-jie Liang Zhi-hua Zhang Li-lin Cui
Dept. of Weaponry Engineering Naval University of Engineering Wuhan China Dept. of Basic Courses Naval University of Engineering Wuhan China Institute of Noise and Vibration Naval University of Engineering Wuhan China
国际会议
长春
英文
386-389
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）