Summary of Towards Joint Graph Learning and Sampling Set Selection From Data, by Shashank N. Sridhara et al.
Towards joint graph learning and sampling set selection from data
by Shashank N. Sridhara, Eduardo Pavez, Antonio Ortega
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Image and Video Processing (eess.IV); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research tackles a crucial problem in graph signal processing, where the graph structure needs to be inferred from data. Traditional methods rely on a two-step process, first learning the graph and then sampling the signals. This study takes a foundational step towards jointly optimizing graph structure and sampling set. The main contribution is Vertex Importance Sampling (VIS), which determines the sampling set from vertex importance obtained through graph learning. Additionally, VISR (Vertex Importance Sampling with Repulsion) is proposed to select important nodes while ensuring better reconstruction. Experimental results on simulated data demonstrate that VIS and VISR achieve competitive reconstruction performance at a lower complexity compared to the conventional two-step approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research focuses on processing signals in graphs where the structure needs to be learned from data. Currently, methods involve learning the graph first and then sampling the signals. This study tries to solve this problem by jointly optimizing the graph and the set of nodes to sample. The main idea is called Vertex Importance Sampling (VIS), which decides what nodes to sample based on how important they are in the graph. Another method, VISR (Vertex Importance Sampling with Repulsion), chooses nodes that are far apart from each other to get better results. The study tested these methods on fake data and found they work well. |
Keywords
» Artificial intelligence » Signal processing
