Volume 66 | Issue 1 | Year 2020 | Article Id. IJMTT-V66I1P533 | DOI : https://doi.org/10.14445/22315373/IJMTT-V66I1P533

The Signal Processing on Graph (SPG) is an emerging field of research aiming to develop accurate methods for big data analysis by combining graph theory and classical signal processing methods. One key method in signal processing on graph is the so-called Graph Fourier Transform (GFT) which is a generalization of the Classical Fourier Transform (defined for data lying on regular domains :1D for times series or 2D for images) to data lying on networks. Those network data are viewed like a set of N interrelated data points lying on a graph whose graph vertices map the data points and graph links encode the relationship between data. In the classical framework, the Fourier transform is a linear operator that performs the mapping of a vector from its initial representation domain to the frequency domain through the Fourier matrix which is an orthonormal basis formed by complex exponential vectors constructed from powers of the complex number ω =e2πi/N. Those vectors are of a key importance in the properties of the transform and its applications. However, for each graph Fourier transform proposed in the literature, although its graph Fourier matrix is orthonormal, its vectors are not complex as in the classical framework, limiting the extension and the use of some useful properties of the classical Fourier transform to the graph signals framework. In this work, we present a method to define a complex orthonormal basis for the graph Fourier transform that allows to perform spectral analysis for graph signals in the frequency domain. The graph Fourier basis we defined is identical to the Fourier basis when applied to graph signals defined on a regular domain. We applied the proposed method successfully to signal detection on an irregularly sampled sensor network.

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Rigobert Fokam, Laurent Bitjoka, Hypolyte Egole, "Complex Basis For Spectral Analysis of Graph Signals," *International Journal of Mathematics Trends and Technology (IJMTT)*, vol. 66, no. 1, pp. 248-260, 2020. *Crossref*, https://doi.org/10.14445/22315373/IJMTT-V66I1P533