title: Foley-Sammon optimal discriminant vectors using kernel approach.
abstract: 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.
authors.affiliation: Engineering Research Center of Information Processing and Application, Southeast University, Nanjing, Jiangsu 210096, China. wenming_zheng@seu.edu.cn
authors.forename: Wenming
authors.initials: W
authors.lastname: Zheng
authors.forename: Li
authors.initials: L
authors.lastname: Zhao
authors.forename: Cairong
authors.initials: C
authors.lastname: Zou
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016002:Discriminant Analysis; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity