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.


Published in:
Proceedings of Asilomar Conference on Signals, Systems, and Computers, 1003-1007
Presented at:
52nd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Oct 28-Nov 01, 2018
Year:
Jan 01 2018
Publisher:
New York, IEEE
ISSN:
1058-6393
ISBN:
978-1-5386-9218-9
Keywords:
Laboratories:




 Record created 2019-06-18, last modified 2019-08-12


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