Offline Kernel Analysis
Yuichi Motai · 2015
Principal composite kernel feature analysis (PC-KFA) is presented to show kernel adaptations for nonlinear features of medical image datasets (MID) in computer-aided diagnosis (CAD). The proposed algorithm PC-KFA has extended the existing studies on kernel feature analysis (KFA), which extracts salient features from a sample of unclassified patterns by use of a kernel method. The principal composite process for PC-KFA herein has been applied to kernel principal component analysis (KPCA) (2002) and to our previously developed accelerated kernel feature analysis (AKFA) (2006). Unlike other kernel-based feature selection algorithms, PC-KFA iteratively constructs a linear subspace of a high-dimensional feature space by maximizing a variance condition for the nonlinearly transformed samples, which we call the data-dependent kernel approach. The resulting kernel subspace can be first chosen by principal component analysis (PCA) and then processed for a composite kernel subspace through the efficient combination representations used for further reconstruction and classification. Numerical experiments based on several MID feature spaces of cancer CAD data have shown that PC-KFA generates an efficient and effective feature representation and has yielded a better classification performance for the proposed composite kernel subspace using a simple pattern classifier.