Graph Heat Mixture Model Learning
| Autor: | Mireille El Gheche, Hermina Petric Maretic, Pascal Frossard |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Social and Information Networks (cs.SI)
FOS: Computer and information sciences Computer Science - Machine Learning Theoretical computer science graph learning Computer science Machine Learning (stat.ML) Computer Science - Social and Information Networks 020206 networking & telecommunications 02 engineering and technology Mixture model Graph Machine Learning (cs.LG) Generative model network inference Statistics - Machine Learning multiple graph learning 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Heat equation Cluster analysis graph mixture model |
| Zdroj: | ACSSC |
| DOI: | 10.1109/acssc.2018.8645150 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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