基于核Fisher判别分析的目标识别

核Fisher判别分析是基于Fisher线性判别提出的一种非线性分类方法，其主要思想是首先把样本映射到某一特征空间，然后在此特征空间进行Fisher线性判别，这样就隐含地实现了原输入空间的非线性判别. 分析了核Fisher判别方法的分类机理，然后基于此方法对三类实际的船舶目标噪声谱进行了识别，并与神经网络、支撑矢量机等其他分类方法做了比较. 实验结果表明，核Fisher判别分析(加上一线性支撑矢量机做阈值估计)的识别效果优于其他分类算法.

Target recognition based on kernel Fisher discriminant

The recognition of the actual ship noises based on the kernel Fisher discriminant analysis is investigated. The main idea of this method is to find a nonlinear direction by first mapping the data nonlinearly into some feature space and compute Fisher's linear discriminant there, thus implicitly yielding a nonlinear discriminant in input space. The mechanism of the kernel Fihser discriminant analysis is particularly analyzed and the classification algorithm is developed. The recognition results are encouraging. In addition, this method is compared with other state-of-the-art classification techniques. Experiment shows that the kernel Fisher discriminant(plus a linear support vector machine to estimate the threshold) is superior to other algorithms.