Title: Learning graphs from data: A signal representation perspective,
Abstract: The construction of a meaningful graph topology plays a crucial role in the
effective representation, processing, analysis and visualization of structured
data. When a natural choice of the graph is not readily available from the data
sets, it is thus desirable to infer or learn a graph topology from the data. In
this tutorial overview, we survey solutions to the problem of graph learning,
including classical viewpoints from statistics and physics, and more recent
approaches that adopt a graph signal processing (GSP) perspective. We further
emphasize the conceptual similarities and differences between classical and
GSP-based graph inference methods, and highlight the potential advantage of the
latter in a number of theoretical and practical scenarios. We conclude with
several open issues and challenges that are keys to the design of future signal
processing and machine learning algorithms for learning graphs from data.