预筛选PCA法在特征分类中的应用

Application of Pre-screening PCA in Feature Classification

主成分分析(PCA)法在特征融合过程中未考虑特征之间特性对分类识别的影响,导致降维后特征无法正常有效分离,因此提出预筛选PCA方法以提取信号的时域特征、频域特征。利用相关系数法可有效区分对象之间的相互关系,先去掉不利于分类的特征,然后对新得到的矩阵进行PCA降维,把时域特征及频域多特征转化为综合性的评价指标,以获得更好的分类效果。结果表明:该方法的特征分离效果更好。研究结果有利于提高PCA分类识别准确率。

For principal component analysis(PCA)method,the influence of features on classification and recognition is not considered in the process of features fusion,which leads to the abnormal and non-effective separation of features after dimensionality reduction.Therefore,a pre-screening PCA method was proposed to extract the time domain features and frequency domain features of signals.The correlation coefficient method was used to distinguish the relationship between objects effectively.The features that were not conducive to classification were removed,and the PCA dimension reduction was performed on the newly obtained matrix to transform the time-domain features and frequency-domain multi-features into comprehensive evaluation indicators,so as to obtain better classification effect.The results show that this method has better feature separation effect.The results are helpful to improve the accuracy of PCA classification.