Electrical Engineering and Systems Science > Signal Processing
arXiv:2210.16272 (eess)
[Submitted on 28 Oct 2022]
Title:Learning with Multigraph Convolutional Filters
View a PDF of the paper titled Learning with Multigraph Convolutional Filters, by Landon Butler and 2 other authors
View PDFAbstract:In this paper, we introduce a convolutional architecture to perform learning when information is supported on multigraphs. Exploiting algebraic signal processing (ASP), we propose a convolutional signal processing model on multigraphs (MSP). Then, we introduce multigraph convolutional neural networks (MGNNs) as stacked and layered structures where information is processed according to an MSP model. We also develop a procedure for tractable computation of filter coefficients in the MGNN and a low cost method to reduce the dimensionality of the information transferred between layers. We conclude by comparing the performance of MGNNs against other learning architectures on an optimal resource allocation task for multi-channel communication systems.
| Comments: | arXiv admin note: text overlap witharXiv:2209.11354 |
| Subjects: | Signal Processing (eess.SP); Machine Learning (cs.LG) |
| Cite as: | arXiv:2210.16272 [eess.SP] |
| (orarXiv:2210.16272v1 [eess.SP] for this version) | |
| https://doi.org/10.48550/arXiv.2210.16272 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Learning with Multigraph Convolutional Filters, by Landon Butler and 2 other authors
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