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Learning Sparse Graphs for Prediction of Multivariate Data Processes
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
Uppsala Univ, Div Syst & Control, Dept Informat Technol, S-75237 Uppsala, Sweden..
2019 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 26, no 3, p. 495-499Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE, 2019. Vol. 26, no 3, p. 495-499
Keywords [en]
Partial correlation graphs, multivariate process, sparse graphs, prediction, hyperparameter-free
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-245903DOI: 10.1109/LSP.2019.2896435ISI: 000458852100005Scopus ID: 2-s2.0-85061748115OAI: oai:DiVA.org:kth-245903DiVA, id: diva2:1296465
Note

QC 20190315

Available from: 2019-03-15 Created: 2019-03-15 Last updated: 2019-03-15Bibliographically approved

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Venkitaraman, Arun

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