Eigendecomposition-Free Sampling Set Selection for Graph Signals
arXiv:1809.01827 · doi:10.1109/TSP.2019.2908129
Abstract
This paper addresses the problem of selecting an optimal sampling set for signals on graphs. The proposed sampling set selection (SSS) is based on a localization operator that can consider both vertex domain and spectral domain localization. We clarify the relationships among the proposed method, sensor position selection methods in machine learning, and conventional SSS methods based on graph frequency. In contrast to the conventional graph signal processing-based approaches, the proposed method does not need to compute the eigendecomposition of a variation operator, while still considering (graph) frequency information. We evaluate the performance of our approach through comparisons of prediction errors and execution time.
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- Low-complexity Graph Sampling with Noise and Signal Reconstruction via Neumann Series
- Graph Signal Sampling Under Stochastic Priors
- Graph-signal Reconstruction and Blind Deconvolution for Structured Inputs
- Graph Sampling for Matrix Completion Using Recurrent Gershgorin Disc Shift
- Non-Bayesian Estimation Framework for Signal Recovery on Graphs
- Graph Signal Processing: Modulation, Convolution, and Sampling
- Graph Signal Processing -- Part III: Machine Learning on Graphs, from Graph Topology to Applications
- Graph Blind Deconvolution with Sparseness Constraint
- A Bayesian Perspective on Uncertainty Quantification for Estimated Graph Signals
- On Critical Sampling of Time-Vertex Graph Signals
- Bayesian Design of Sampling Set for Bandlimited Graph Signals
- Sensor selection on graphs via data-driven node sub-sampling in network time series
- Time-Vertex Machine Learning for Optimal Sensor Placement in Temporal Graph Signals: Applications in Structural Health Monitoring
- A low discrepancy sequence on graphs