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Approche bayésienne non paramétrique pour la factorisation de matrice binaire à faible rang avec loi de puissance

Abstract : We introduce a low-rank Bayesian nonparametric (BNP) model for bipartite graphs. Recently, Caron (2012) proposed a BNP model where each node is given its own sociability parameter allowing to capture the power-law behavior of real world bipartite graphs. This model can be considered as a rank one nonnegative factorization of the adjacency matrix. Building on the compound random measures recently introduced by Griffin and Leisen (2014), we derive a rank p generalization of this model where each node is associated with a p-dimensional vector of sociability parameters accounting for several latent dimensions. While preserving the desired properties of interpretability, scalability and power-law behavior, our model is more flexible and provides better predictive performance as illustrated on several datasets.
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Contributor : Adrien Todeschini Connect in order to contact the contributor
Submitted on : Friday, January 15, 2016 - 2:04:33 PM
Last modification on : Wednesday, February 2, 2022 - 3:53:27 PM

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  • HAL Id : hal-01157151, version 2

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Adrien Todeschini, Francois Caron. Approche bayésienne non paramétrique pour la factorisation de matrice binaire à faible rang avec loi de puissance. 47èmes Journées de Statistique de la SFdS, Société Française de Statistique, Jun 2015, Lille, France. ⟨hal-01157151v2⟩

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