2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | SPTM-12.1 | ||
| Paper Title | DESIGN OF GRAPH SIGNAL SAMPLING MATRICES FOR ARBITRARY SIGNAL SUBSPACES | ||
| Authors | Junya Hara, Koki Yamada, Tokyo University of Agriculture and Technology, Japan; Shunsuke Ono, Tokyo Institute of Technology, Japan; Yuichi Tanaka, Tokyo University of Agriculture and Technology, Japan | ||
| Session | SPTM-12: Sampling, Filtering and Denoising over Graphs | ||
| Location | Gather.Town | ||
| Session Time: | Wednesday, 09 June, 16:30 - 17:15 | ||
| Presentation Time: | Wednesday, 09 June, 16:30 - 17:15 | ||
| Presentation | Poster | ||
| Topic | Signal Processing Theory and Methods: [SIPG] Signal and Information Processing over Graphs | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | We propose a design method of sampling matrices for graph signals that guarantees perfect recovery for arbitrary graph signal subspaces. When the signal subspace is known, perfect reconstruction is always possible from the samples with an appropriately designed sampling matrix. However, most graph signal sampling methods so far design sampling matrices based on the bandlimited assumption and sometimes violates the perfect reconstruction condition for the other signal models. In this paper, we formulate an optimization problem for the design of the sampling matrix that guarantees perfect recovery, thanks to a generalized sampling framework for standard signals. In experiments with various signal models, our sampling matrix presents better reconstruction accuracy both for noiseless and noisy situations． | ||