TY - JOUR AU - Loukas, Andreas AU - Perraudin, Nathanaƫl PY - 2019 DA - 2019/08/20 TI - Stationary time-vertex signal processing JO - EURASIP Journal on Advances in Signal Processing SP - 36 VL - 2019 IS - 1 AB - This paper considers regression tasks involving high-dimensional multivariate processes whose structure is dependent on some known graph topology. We put forth a new definition of time-vertex wide-sense stationarity, or joint stationarity for short, that goes beyond product graphs. Joint stationarity helps by reducing the estimation variance and recovery complexity. In particular, for any jointly stationary process (a) one reliably learns the covariance structure from as little as a single realization of the process and (b) solves MMSE recovery problems, such as interpolation and denoising, in computational time nearly linear on the number of edges and timesteps. Experiments with three datasets suggest that joint stationarity can yield accuracy improvements in the recovery of high-dimensional processes evolving over a graph, even when the latter is only approximately known, or the process is not strictly stationary. SN - 1687-6180 UR - https://doi.org/10.1186/s13634-019-0631-7 DO - 10.1186/s13634-019-0631-7 ID - Loukas2019 ER -