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Recursive Prediction of Graph Signals with Incoming Nodes
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0003-1285-8947
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0003-2638-6047
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0002-1927-1690
2020 (English)In: 2020 IEEE International Conference on Acoustics, Speech, And Signal Processing, Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 5565-5569, article id 9053145Conference paper, Published paper (Refereed)
Abstract [en]

Kernel and linear regression have been recently explored in the prediction of graph signals as the output, given arbitrary input signals that are agnostic to the graph. In many real-world problems, the graph expands over time as new nodes get introduced. Keeping this premise in mind, we propose a method to recursively obtain the optimal prediction or regression coefficients for the recently proposed Linear Regression over Graphs (LRG), as the graph expands with incoming nodes. This comes as a natural consequence of the structure of the regression problem, and obviates the need to solve a new regression problem each time a new node is added. Experiments with real-world graph signals show that our approach results in a good prediction performance which tends to be close to that obtained from knowing the entire graph apriori.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2020. p. 5565-5569, article id 9053145
Series
International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Keywords [en]
Linear regression, graph expansion, graph signal processing, recursive least squares, convex optimization
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-292373DOI: 10.1109/ICASSP40776.2020.9053145ISI: 000615970405165Scopus ID: 2-s2.0-85089231874OAI: oai:DiVA.org:kth-292373DiVA, id: diva2:1544734
Conference
2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020; Barcelona; Spain; 4 May 2020 through 8 May 2020
Note

QC 20210416

Available from: 2021-04-16 Created: 2021-04-16 Last updated: 2023-04-05Bibliographically approved

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Venkitaraman, ArunChatterjee, SaikatWahlberg, Bo

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