In this paper, we give a brief review of the theory of spectral analysis of large dimensional random matrices. Most of the existing work in the literature has been stated for real matrices but the corresponding results for the complex case are also of interest, especially for researchers

Despite many empirical successes of spectral clustering methods -- algorithms that cluster points using eigenvectors of matrices derived from the distances between the points -- there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors

under strong additional assumptions, which, as we demonstrate, are not always satisfied in real data. We conclude that our analysis provides strong evidence for the superiority of normalized spectral clustering in practical applications. We believe that methods used in our analysis will provide a basis

Abstract not found

KIMMO KARTASALO: Spectral analysis of oriented features in micrographs

map that is defined on the entire manifold. The framework of analysis

We perform spectral analysis of the Internet topology at the AS level, by adapting the standard spectral filtering method of examining the eigenvectors corresponding to the largest eigenvalues of matrices related to the adjacency matrix of the topology. We observe that the method suggests clusters

This chapter introduces concepts fundamental to spectral analysis and applies spectral analysis to char-acterize neural signals. Spectral analysis is a form of time series analysis and concerns a series of events

Spectral analysis of intracardiac electrograms during induced and spontaneous ventricular fibrillation in humans

Abstract. We present the analysis of Hα spectra obtained during a two-ribbon flare observed on the solar disk on May 16, 1981 at Yunnan Observatory with the Spectra-Spectroheliograph (SSHG). The complicated asymmetric profiles produced by the post-flare loops overlying flare ribbons are analysed