Considering graph signal processing (GSP) as an extended version of classical signal processing, definition of fundamental concepts such as spectral, Fourier transform, generalized operators and so forth for graph setting is highly beneficial and meaningful to get a deep understand of graph signals. In this paper, we concentrate on graph spectral analysis to aim at revealing hidden information of signals (data). In particular, we first define two new operators for GSP including translation and modulation, before introducing a new way to obtain the windowed graph Fourier transform called by namely Short Time Fourier Transform in classical setting. The experiment results indicate that our methods, which are more outstanding than those earlier methods, are much the same as their counterparts in classical signal processing.
Size : 5,63 kB
Format : Adobe PDF
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Considering graph signal processing (GSP) as an extended version of classical signal processing, definition of fundamental concepts such as spectral, Fourier transform, generalized operators and so forth for graph setting is highly beneficial and meaningful to get a deep understand of graph signals. In this paper, we concentrate on graph spectral analysis to aim at revealing hidden information of signals (data). In particular, we first define two new operators for GSP including translation and modulation, before introducing a new way to obtain the windowed graph Fourier transform called by namely Short Time Fourier Transform in classical setting. The experiment results indicate that our methods, which are more outstanding than those earlier methods, are much the same as their counterparts in classical signal processing.
Size : 5,63 kB
Format : Adobe PDF
