Kadambari, SK and Chepuri, SP (2020) Learning product graphs from multidomain signals. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 4-8 May 2020, Barcelona; Spain, pp. 5665-5669.
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Abstract
In this paper, we focus on learning the underlying product graph structure from multidomain training data. We assume that the product graph is formed from a Cartesian graph product of two smaller factor graphs. We then pose the product graph learning problem as the factor graph Laplacian matrix estimation problem. To estimate the factor graph Laplacian matrices, we assume that the data is smooth with respect to the underlying product graph. When the training data is noise free or complete, learning factor graphs can be formulated as a convex optimization problem, which has an explicit solution based on the water-filling algorithm. The developed framework is illustrated using numerical experiments on synthetic data as well as real data related to air quality monitoring in India. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
| Item Type: | Conference Paper |
|---|---|
| Publication: | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Additional Information: | cited By 0; Conference of 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 ; Conference Date: 4 May 2020 Through 8 May 2020; Conference Code:161907 |
| Keywords: | Air quality; Audio signal processing; Convex optimization; Graph algorithms; Laplace transforms; Matrix algebra; Speech communication, Air quality monitoring; Convex optimization problems; Explicit solutions; Learning factor; Learning problem; Learning products; Numerical experiments; Water-filling algorithm, Graph structures |
| Department/Centre: | Division of Electrical Sciences > Electrical Communication Engineering |
| Date Deposited: | 06 Jan 2021 06:17 |
| Last Modified: | 06 Jan 2021 06:17 |
| URI: | http://eprints.iisc.ac.in/id/eprint/66771 |
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