A simple estimator of the correlation kernel matrix of a determinantal point process
The Determinantal Point Process (DPP) is a parameterized model for multivariate binary variables, characterized by a correlation kernel matrix. This paper proposes a closed form estimator of this kernel, which is particularly easy to implement and can also be used as a starting value of learning algorithms for maximum likelihood estimation. We prove the consistency and asymptotic normality of our estimator, as well as its large deviation properties.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Fixed-point algorithms for learning determinantal point processes
Determinantal point processes (DPPs) offer an elegant tool for encoding probabilities over subsets of a ground set. Discrete DPPs are parametrized by a positive semidefinite matrix (called the DPP kernel), and estimating…
Point ProcessesExpectation-Maximization for Learning Determinantal Point Processes
A determinantal point process (DPP) is a probabilistic model of set diversity compactly parameterized by a positive semi-definite kernel matrix. To fit a DPP to a given task, we would like to learn the entries of its ker…
DiversityPoint ProcessesProduct RecommendationLarge-Margin Determinantal Point Processes
Determinantal point processes (DPPs) offer a powerful approach to modeling diversity in many applications where the goal is to select a diverse subset. We study the problem of learning the parameters (the kernel matrix) …
Diversityparameter estimationPoint ProcessesVideo SummarizationDeep Determinantal Point Processes
Determinantal point processes (DPPs) have attracted significant attention as an elegant model that is able to capture the balance between quality and diversity within sets. DPPs are parameterized by a positive semi-defin…
DiversityPoint ProcessesOn two ways to use determinantal point processes for Monte Carlo integration
When approximating an integral by a weighted sum of function evaluations, determinantal point processes (DPPs) provide a way to enforce repulsion between the evaluation points. This negative dependence is encoded by a ke…
Numerical IntegrationPoint Processes