Learning Sparse Graphs for Prediction of Multivariate Data Processes | IEEE Journals & Magazine | IEEE Xplore

Learning Sparse Graphs for Prediction of Multivariate Data Processes


Abstract:

We address the problem of prediction of multivariate data process using an underlying graph model. We develop a method that learns a sparse partial correlation graph in a...Show More

Abstract:

We address the problem of prediction of multivariate data process using an underlying graph model. We develop a method that learns a sparse partial correlation graph in a tuning-free and computationally efficient manner. Specifically, the graph structure is learned recursively without the need for cross validation or parameter tuning by building upon a hyperparameter-free framework. Our approach does not require the graph to be undirected and also accommodates varying noise levels across different nodes. Experiments using real-world datasets show that the proposed method offers significant performance gains in prediction, in comparison with the graphs frequently associated with these datasets.
Published in: IEEE Signal Processing Letters ( Volume: 26, Issue: 3, March 2019)
Page(s): 495 - 499
Date of Publication: 30 January 2019

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