  This paper addresses the problem of selecting an optimal sampling set for
signals on graphs. The proposed sampling set selection (SSS) is based on a
localization operator that can consider both vertex domain and spectral domain
localization. We clarify the relationships among the proposed method, sensor
position selection methods in machine learning, and conventional SSS methods
based on graph frequency. In contrast to the conventional graph signal
processing-based approaches, the proposed method does not need to compute the
eigendecomposition of a variation operator, while still considering (graph)
frequency information. We evaluate the performance of our approach through
comparisons of prediction errors and execution time.
