Foley-Sammon optimal discriminant vectors using kernel approach
Foley-Sammon optimal discriminant vectors using kernel approach
复制标题
DOI：
10.1109/tnn.2004.836239
复制
发表时间：
2005
影响因子：
--
通讯作者：
Wenming Zheng;Li Zhao;C. Zou
中科院分区：
文献类型：
--
作者：
Wenming Zheng;Li Zhao;C. Zou
提出了一种新的非线性特征提取方法--核Foley-Sammon最优鉴别向量（KFSODVs）。该方法利用支持向量机（SVM）和其他常用核学习算法中的核技巧，将Foley-Sammon最优鉴别向量（FSODV）从线性域扩展到非线性域。该方法还提供了一种有效的技术，以解决所谓的小样本大小（SSS）的问题，存在于许多分类问题，如人脸识别。我们给出了KFSODV的推导过程，并在模拟数据集和真实的数据集上进行了实验，证实了KFSODV方法在判别性能方面优于以往常用的基于核的学习算法上级。
A new nonlinear feature extraction method called kernel Foley-Sammon optimal discriminant vectors (KFSODVs) is presented in this paper. This new method extends the well-known Foley-Sammon optimal discriminant vectors (FSODVs) from linear domain to a nonlinear domain via the kernel trick that has been used in support vector machine (SVM) and other commonly used kernel-based learning algorithms. The proposed method also provides an effective technique to solve the so-called small sample size (SSS) problem which exists in many classification problems such as face recognition. We give the derivation of KFSODV and conduct experiments on both simulated and real data sets to confirm that the KFSODV method is superior to the previous commonly used kernel-based learning algorithms in terms of the performance of discrimination.