|Date:||Wed, November 2, 2016|
|Place:||Research I Seminar Room|
Abstract: In many signal processing applications, such as social and economic networks, brain imaging, epidemiology and traffic networks, high dimensional data is naturally associated with the vertices of a weighted graph that represents the relations between data units. Since such data should be processed and analyzed taking these relations into account, extending basic operators and signal processing methods to the case when the underlying domain of a signal is a graph is one of the main challenges. I am particularly interested in developing time-frequency analysis in the setup of graph-based signals. In the talk we are going to discuss how a "graph based" time-frequency representation of signals can be defined taking into account both underlying graph structure and "classical" properties.