Learning sparse models of diffusive graph signals
(2017) 25th European Symposium on Artificial Neural Networks — Location: Bruges, Belgium (26.April.2017)
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- Abstract
- Graph signals that describe data living on irregularly structured domains provide a generic representation for structured information in very diverse applications. The effective analysis and processing of such signals however necessitate good models that identify the most relevant signal components. In this paper, we propose to learn sparse representation models for graph signals that describe heat diffusion processes. This consists in learning a dictionary that incorporates spectral properties of an implicit graph diffusion kernel. The underlying formulation enables the identification of both sparse features and an adaptive graph structure from mere signal observations. Experiments on synthetic and real datasets show that the proposed dictionaries not only reflect the underlying diffusion process but also significantly reduce over-fitting of data in comparison to state-of-the-art methods.
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Citations
Dong, S., Thanou, D., Absil, P.-A., & Frossard, P. (2017). Learning sparse models of diffusive graph signals. Computational Intelligence and Machine learning, p. 251-256. https://hdl.handle.net/2078.5/253722