Duality induced by an embedding structure of determinantal point process
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Abstract (arXiv)
This paper investigates the information geometrical structure of a determinantal point process (DPP). It demonstrates that a DPP is embedded in the exponential family of log-linear models. The extent of deviation from an exponential family is analyzed using the e-embedding curvature tensor, which identifies partially flat parameters of a DPP. On the basis of this embedding structure, the duality related to a marginal kernel and an L-ensemble kernel is discovered.
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References · 8
- 2016Monte Carlo with Determinantal Point Processescited by 22
- 2014Learning the Parameters of Determinantal Point Process Kernelscited by 21
- 2014Expectation-Maximization for Learning Determinantal Point Processescited by 19
- 2016Learning Determinantal Point Processes in Sublinear Timecited by 7
- 2016Kronecker Determinantal Point Processescited by 5
- 2022Hardness of Maximum Likelihood Learning of DPPscited by 3
- 2014Diversifying Sparsity Using Variational Determinantal Point Processescited by 2
- 2022Minimum information dependence modelingcited by 1
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