基于局域判别基空间能量的特征提取

Feature Extraction Based on Subspace Energy of Local Discriminant Basis

针对模式识别中如何提取信号有效特征的问题,对信号进行小波包分解,求取小波包局域判别基,提出求取局域判别基的各子空间的能量,形成特征矢量的特征提取方法。利用Fisher准则函数进行特征选择,得到识别特征矢量。在水声模式识别实例中应用此方法提取特征矢量进行分类实验,取得良好的分类效果,验证了该方法的有效性。

In order to obtain the effectual feature of signals, wavelet packet transform is used. The characters of every wavelet packet basis are different, which can express the main feature of a signal. The local discriminant basis （LDB） is calculated based on the distance criterion, and a feature extraction method is proposed. The feature vector, which expresses the energy of sub - space in LDB, is obtained by using Fisher criterion for feature choice. The classification experiment for three different classes of targets is done. The results of the experiment show that this feature extraction method is effectual in pattern recognition.