Multi-View Weighted Network
Contribuinte(s) |
Mukherjee, Sayan |
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Data(s) |
2016
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Resumo |
<p>Extensive investigation has been conducted on network data, especially weighted network in the form of symmetric matrices with discrete count entries. Motivated by statistical inference on multi-view weighted network structure, this paper proposes a Poisson-Gamma latent factor model, not only separating view-shared and view-specific spaces but also achieving reduced dimensionality. A multiplicative gamma process shrinkage prior is implemented to avoid over parameterization and efficient full conditional conjugate posterior for Gibbs sampling is accomplished. By the accommodating of view-shared and view-specific parameters, flexible adaptability is provided according to the extents of similarity across view-specific space. Accuracy and efficiency are tested by simulated experiment. An application on real soccer network data is also proposed to illustrate the model.</p> Thesis |
Identificador | |
Palavras-Chave | #Statistics #Multi-View #Network #Weighted |
Tipo |
Thesis |