Document worth reading: “Eigenvalue and Generalized Eigenvalue Problems: Tutorial”

This paper is a tutorial for eigenvalue and generalized eigenvalue points. We first introduce eigenvalue draw back, eigen-decomposition (spectral decomposition), and generalized eigenvalue draw back. Then, we level out the optimization points which yield to the eigenvalue and generalized eigenvalue points. We moreover current examples from machine learning, along with principal half analysis, kernel supervised principal half analysis, and Fisher discriminant analysis, which finish in eigenvalue and generalized eigenvalue points. Finally, we introduce the choices to every eigenvalue and generalized eigenvalue points. Eigenvalue and Generalized Eigenvalue Problems: Tutorial