提出一种简单有效的DPP相关核矩阵估计方法。
A simple estimator of the correlation kernel matrix of a determinantal point process
- 基于闭式解直接估算DPP的相关核矩阵。
- 证明了估计量的一致性、渐近正态性和大偏差性质。
- 适合需要快速初始化或简化学习流程的研究者使用。
确定性点过程（DPP）是一种用于多变量二值变量的参数化模型，其特征由相关核矩阵定义。本文提出了一种该核矩阵的闭式估计方法，实现简单，可作为最大似然估计学习算法的初始值。我们证明了该估计量的一致性、渐近正态性以及大偏差性质。
原文摘要 · Abstract (English)
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.
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