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Found 7 papers, 1 papers with code
A Generalization of the Convolution Theorem and its Connections to Non-Stationarity and the Graph Frequency Domain
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 filters.
Forecasting Graph Signals with Recursive MIMO Graph Filters
Forecasting time series on graphs is a fundamental problem in graph signal processing.
Blind Polynomial Regression
Fitting a polynomial to observed data is an ubiquitous task in many signal processing and machine learning tasks, such as interpolation and prediction.
Learning Time-Varying Graphs from Online Data
This work proposes an algorithmic framework to learn time-varying graphs from online data.
Online Time-Varying Topology Identification via Prediction-Correction Algorithms
Signal processing and machine learning algorithms for data supported over graphs, require the knowledge of the graph topology.
Topology-Aware Joint Graph Filter and Edge Weight Identification for Network Processes
Therefore, in this paper, we focus on the joint identification of coefficients and graph weights defining the graph filter that best models the observed input/output network data.
Forecasting Multi-Dimensional Processes over Graphs
The forecasting of multi-variate time processes through graph-based techniques has recently been addressed under the graph signal processing framework.
