ADAPTIVE SUBSAMPLING OF MULTIDOMAIN SIGNALS WITH PRODUCT GRAPHS

Theo Gnassounou, Pierre Humbert, Laurent Oudre

Abstract:

In this paper, we propose an adaptive subsampling method for multidomain signals based on the constrained learning of a product
graph. Given an input multidomain signal, we search for a product
graph on which the signal is bandlimited, i.e. have limited spectral
occupancy. The subsampling procedure described in this article
is composed of two successive steps. First, we use the input data
to learn a graph that will be optimized to favor efficient sampling.
Then, we derive an algorithm for choosing the best nodes and provide a sampling strategy for multidomain signals. Experiments on
synthetic data and two real datasets show the efficiency of the proposed method and its relevance for multidomain data compression
and storing.