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MULTI-KERNEL REGRESSION FOR GRAPH SIGNAL PROCESSING
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0003-2638-6047
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0002-2718-0262
2018 (English)In: 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE, 2018, p. 4644-4648Conference paper, Published paper (Refereed)
Abstract [en]

We develop a multi-kernel based regression method for graph signal processing where the target signal is assumed to be smooth over a graph. In multi-kernel regression, an effective kernel function is expressed as a linear combination of many basis kernel functions. We estimate the linear weights to learn the effective kernel function by appropriate regularization based on graph smoothness. We show that the resulting optimization problem is shown to be convex and propose an accelerated projected gradient descent based solution. Simulation results using real-world graph signals show efficiency of the multi-kernel based approach over a standard kernel based approach.

Place, publisher, year, edition, pages
IEEE, 2018. p. 4644-4648
Keywords [en]
Graph signal processing, kernel regression, convex optimization
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-237154DOI: 10.1109/ICASSP.2018.8461643ISI: 000446384604162Scopus ID: 2-s2.0-85054280684OAI: oai:DiVA.org:kth-237154DiVA, id: diva2:1258680
Conference
2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Note

QC 20181025

Available from: 2018-10-25 Created: 2018-10-25 Last updated: 2019-08-20Bibliographically approved

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Venkitaraman, ArunChatterjee, SaikatHändel, Peter

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Output format
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