… Linear discriminant analysis (LDA) is a widely used … This paper also develops a complete
framework for KFDA in … order by the face feature extracted by KPCA and two palmpring features …

… a complete KPCA plus LDA solution to extract discriminant … by complete KECA+LDA and
that by complete KPCA+LDA. … task of discriminative feature extraction, a discriminant kernel …

… This paper aims to introduce a comprehensive data-driven … analysis (PCA) and Kernel PCA
(KPCA), have been effectively … feature extraction. This stage focuses on uncovering relevant …

… Feature extraction is a significant knowledge. Kernel principal component analysis (KPCA) …
difficult to achieve desired results when used for complex and nonlinear feature extraction. …

… ) [16], which is a supervised method for feature extraction and … of interest in the sufficient
dimension reduction framework. … methods PCA (PCA, Kernel PCA), LDA and ICA. MATLAB 2011 …

… is a promising technique for feature extraction from MRIs, as it … PCA), KPCA, linear discriminant
analysis (LDA) and singular … and DMK-SVM framework. With regular SVM we use an RBF …

… We consider the kernel-based DR methods KPCA, SKPCA, … Detailed documentation of our
feature extraction process can … Within the framework of feature extraction, dimension reduction…

… Linear discriminant analysis (LDA) is a classical linear … fall into graph Laplacian-based
framework, applying the Laplacian … kernel Hilbert space (RKHS) by kernel method in this paper. …

… The use of kernel technique in the framework of machine learning … kernel methods: Kernel
PCA (KPCA) [22], Complete Kernel Fisher Discriminant (CKFD) [34], Regularized Kernel LDA (…

… framework to formulate component analysis methods, including PCA-like, linear discriminant
analysis (LDA… in data by using the kernel method similar to kernel PCA. All these extensions …