Graph Heat Mixture Model Learning
Graph inference methods have recently attracted a great interest from the scientific community, due to the large value they bring in data interpretation and analysis. However, most of the available state-of-the-art methods focus on scenarios where all available data can be explained through the same graph, or groups corresponding to each graph are known a priori. In this paper, we argue that this is not always realistic and we introduce a generative model for mixed signals following a heat diffusion process on multiple graphs. We propose an expectation-maximisation algorithm that can successfully separate signals into corresponding groups, and infer multiple graphs that govern their behaviour. We demonstrate the benefits of our method on both synthetic and real data.
WOS:000467845100177
2018-01-01
978-1-5386-9218-9
New York
Conference Record of the Asilomar Conference on Signals Systems and Computers
1003
1007
REVIEWED
Event name | Event place | Event date |
Pacific Grove, CA | Oct 28-Nov 01, 2018 | |