Feature Extraction Using a Complete Kernel Extension of Supervised Graph Embedding

In this article, the kernel-based methods explained by a graph embedding framework are analyzed and their nature is revealed, i.e. any kernel-based method in a graph embedding framework is equivalent to kernel principal component analysis plus its corresponding linear one. Based on this result, the authors propose a complete kernel-based algorithms framework. Any algorithm in our framework makes full use of two kinds of discriminant information, irregular and regular. The proposed algorithms framework is tested and evaluated using the ORL, Yale and FERET face databases. The experiment results demonstrate the effectiveness of our proposed algorithms framework.