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Learning Sparse Graphs for Prediction of Multivariate Data Processes
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DOI：10.1109/LSP.2019.2896435.png)
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Abstract
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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.
Keywords:
Partial correlation graphs
multivariate process
sparse graphs
prediction
hyperparameter-free
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