  This paper investigates the active sampling for estimation of approximately
bandlimited graph signals. With the assistance of a graph filter, an
approximately bandlimited graph signal can be formulated by a Gaussian random
field over the graph. In contrast to offline sampling set design methods which
usually rely on accurate prior knowledge about the model, unknown parameters in
signal and noise distribution are allowed in the proposed active sampling
algorithm. The active sampling process is divided into two alternating stages:
unknown parameters are first estimated by Expectation Maximization (EM), with
which the next node to sample is selected based on historical observations
according to predictive uncertainty. Validated by simulations compared with
related approaches, the proposed algorithm can reduce the sample size to reach
a certain estimation accuracy.
