Learning Sparse Graphs for Prediction and Filtering of Multivariate Data Processes

Arun Venkitaraman; Dave Zachariah

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arXiv · 1712.04542

Learning Sparse Graphs for Prediction and Filtering of Multivariate Data Processes

Arun Venkitaraman·Dave Zachariah

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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.

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Arun Venkitaraman, Dave Zachariah. 2017-12-12. Learning Sparse Graphs for Prediction and Filtering of Multivariate Data Processes. https://arxiv.org/abs/1712.04542

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