1 citations · 1 across the 4 of their papers we have counts for
6 papers · 1 filter
A Generalization of the Convolution Theorem and its Connections to Non-Stationarity and the Graph Frequency Domain
Alberto Natali, Geert Leus
In this paper, we present a novel convolution theorem which encompasses the well known convolution theorem in (graph) signal processing as well as the one related to time-varying f…
Forecasting Graph Signals with Recursive MIMO Graph Filters
Jelmer van der Hoeven, Alberto Natali, Geert Leus
Forecasting time series on graphs is a fundamental problem in graph signal processing. When each entity of the network carries a vector of values for each time stamp instead of a s…
Blind Polynomial Regression
Alberto Natali, Geert Leus
Fitting a polynomial to observed data is an ubiquitous task in many signal processing and machine learning tasks, such as interpolation and prediction. In that context, input and o…
Online Time-Varying Topology Identification via Prediction-Correction Algorithms
Alberto Natali, Mario Coutino, Elvin Isufi +1
Signal processing and machine learning algorithms for data supported over graphs, require the knowledge of the graph topology. Unless this information is given by the physics of th…
Topology-Aware Joint Graph Filter and Edge Weight Identification for Network Processes
Alberto Natali, Mario Coutino, Geert Leus
Data defined over a network have been successfully modelled by means of graph filters. However, although in many scenarios the connectivity of the network is known, e.g., smart gri…
Forecasting Multi-Dimensional Processes over Graphs
Alberto Natali, Elvin Isufi, Geert Leus
The forecasting of multi-variate time processes through graph-based techniques has recently been addressed under the graph signal processing framework. However, problems in the rep…